CN7000 Dissertation Sample

Module Title And Code – CN7000 Dissertation Sample

1.1 Introduction.

This research paper majorly states about the implementation of “artificial intelligence” i.e. AI into an attendance system by the use of Python. Facial recognition system is one of the advanced technologies that helps in recognizing a person effectively without making any physical contact.

Thus this system can be introduced into the traditional attendance system, which will help in checking in and checking out of the authorized personnel into particular areas where the system is being installed and the accuracy in results is being obtained from the use of Python. Thus, this system will also help in enhancing the overall security concerns and will also help in real tracking of the authorized persons into the commercial areas.

However, this system is completely different from the face detection system which helps in objectifying the human face in order to properly identify humans. Moreover, this system is being used for attaining the complete security concerns that are being governed from the data violations and which can make concise as well as precise timely attendance into the framework.

In fact, this system requires less time in making as well as evaluating the data of a single person’s attendance for the month. Hence, this system also provides warning signals through the help of a complete framework and which also restricts unauthorized persons from entering into that particular area.

Thus, implementation of this system will help the organizations in reducing the precious time that is being executed into the calculating of the attendances manually and also helps in improving the productivity of the persons who are a part of this system. Furthermost, this chapter consists of following sub parts which will help to give clear understanding on which the entire research paper that is being conducted.

1.2 Problem Statement.

The traditional methods which were used for taking attendance were often raised with several issues like data manipulation or missing, which was one of the major concerns in making of the required payment to the respective employees or allocating good scores to the students’ behaviors. Thus this system FR i.e. Facial Recognition helps in resting any obligations or any sort of misconducts which is being carried while making of the entire attendance (Mandloi 2019).

In fact this is one of the best systems which can be installed into the universities where the precise evaluations of the student’s attendance can be made and which can also prove a daily analysis on which the student is interested in doing the class. Thus AI will help to self-train the FR system from the captured images in order to give better results.

Moreover, this FR system can also be used for those areas where confidential works are being executed and which permits only authorized personnel’s, which helps in maintaining the overall confidentiality of the product or the work which is being executed. Furthermore, this study has been created for transforming the traditional methods that are being used for taking attendance into a new modern technique which will allow the system to digitally conduct attendance by identifying the persons at a particular time.

1.3 Research Aim.

The research aim which is being associated with the topic of the research paper is to make a complete understanding of the process which is being associated with this FR technologies which completely helps in conducting effective and timely attendance with respect to the huge analysis which can be attained in the form of graphs. Moreover the process which is being involved while executing AI based FR systems in conducting effective attendance has also been discussed in the entire research paper.

1.4 Research Objective.

In order to resolve the drawbacks which are being faced by the FR system, a set of objectives has been stated into the following points (Gopila and Prasad 2020). Which will allow to restrict the traditional ways that are being procured in executing the attendances and will be able to opt for this system in order to make efficient and effective attendances which will also help to procure the accuracy in data.

To develop a smart and portable attendance system which is easy to use and is self-powered.
To ensure speedy recording of attendances for less than three seconds.
To have a huge memory space to store the data effectively.
To create a user friendly database in managing attendances.
To investigate the proper identification which shows the entire system is successful or not.

1.5 Research Rationale.

The traditional ways in which the attendances are being taken into the specified organization like education institutions, government offices, etc. faces tremendous difficulties in taking as well as executing the attendances and also faces major difficulties in stopping the unauthorized persons into a specified zone. So in order to enhance the overall concerns which are being faced by the concerned authorities by introducing the AI based technology which helps in making of the FR , which easily detects the persons which will be allowed to enter into the premises or to take their attendances by the help of python programming language.

However, this system helps in maintaining a precise record into the entire excel sheets automatically from where data are being used for the useful transmission of the graphs and charts which will help to determine the individual performance of the people (Huang et al. 2021).

This AI based attendance system requires huge technologically enabled things which will make this process to run smoothly like 24 by 7 strong internet connectivity into the different parts of the organization where this system is to be introduced along with the CCTV monitoring cameras, database management system where all the data’s are being kept and which are being used for the future purposes.

Moreover a backup room is to be created in which the entire room will serve the electrical backup during any breakup. Moreover, introduction of this hi tech system will help the majority of the organizations in maintaining their most useful record i.e. attendances through a contactless process.

1.6 Research Significance.

There are huge software’s as well as mechanisms which are being implemented into the organization for making or conducting of the effective attendances which will make the people to be aware of their time beings and will particularly perform as per the organizational overview. Which also lacks in many major areas like live tracking and monitoring.

Thus this AI based FR system which is being executed through the Python, not only helps in taking crucial attendances into an specified time beings but also helps in resting the unnecessary peoples to enter into the entire organization, tracking the behavior or the places where the people mostly spend their time, etc.

moreover, this system which is being going to be discussed into the entire research paper are also having some drawbacks like it can’t be applicable in “Work From Home based Model”, as it can track the peoples face for making attendances and hence the organizations have to opt for the other systems which will help to conduct attendances (Panigrahi 2020).

Thus, this research paper is being conducted in which the entire process which is being involved in effectively taking off their attendance of the people who are involved into certain activities of the organization through the FR system which critically uses python. [Referred to Appendix 1.0]

1.7 Research Framework.

The research framework which is being used for completing this entire paper have been depicted through the below figure in which the entire sections which have been divided as well as provided systematically in order to understand about the entire paper.

Figure 1: Research Framework. (Source: Self-created in draw.io)

1.8 Conclusion.

This is the summarization part of the entire paper on which the entire chapter one has been discussed as per the given topic in which the entire research work has to be conducted. In the introduction part the crucial aspects on which the entire topic has been discussed which helped in denoting the entire process in which the study has to be conducted.

Which is being followed by the crucial discussion of the section i.e. background of the study which helped in revealing of the issues which have been faced by the traditional methods while taking attendance and the benefits which will be attained in opting for the AI based FR system which is used by Python.

Moreover, this chapter also consists of the research aim which helps in stating about the aim which is being required for conducting this research paper, which is also followed by the research objectives which will be discussed briefly in the methodological section of this research paper.

Furthermost, this research paper also consists of the research rationale and significance in which the entire process which will help in benefitting the organizations which will opt for this technological change have been discussed which also followed with the research framework which helps in depicting the entire framework of the research paper in which the different sections have been discussed for fulfilling as well as meeting of the entire requirements which will make the entire research paper fruitful in getting all the knowledge’s which are being required to understand the process.

2.0 Literature Review.

2.1 Introduction.

This chapter i.e. literature review helps in providing detailed information about the benefits as well as problems which are being completely faced while executing this research work and which is completely associated with the areas of research. However, this chapter helps in providing the detailed insight of the research papers which are being published into the scholar websites as well as from the different secondary sources which will help to enhance the complete understanding as well as acquiring of the overall knowledge which have been used into their implementations.

Moreover, this chapter also helps in demonstrating the different theories as well as models which are being used by the researchers in their research work which also helped in conducting the subjective evaluation from the secondary sources. Which is forwarded by the gaps which have been mentioned into the literature section along with the conceptual framework which is being successfully presented into this chapter.

2.2 Empirical Study.

This section helps in providing the initial understandings from the critical reviews of the entered research papers which have been published by the different authors. Which critically mentions about the approaches which have been used by the researchers into their particular research work, which helps in gaining of the complete knowledge which is being used to proceed into the overall research work. Thus, below are the reviews of the different sections which have been used for supporting the overall context of the entire paper.

According to Siddiqui 2020, In order to enhance the overall security by AI based face detection and Face recognition system has been discussed by the author. Which states about the major changes which completely comes across the facial recognition in order to find the gender as well as age of the respective individuals. Which is majorly combined with a face detection with respect to the voice and biometric technology.

However, the author also stated about the real life implementation of the process into a university where the details of the students along with the faculties have been provided to the database in order to train the system so as to easily recognize the peoples effectively so as to mark their attendances.

Moreover, the useful findings which have been presented for the detection of the faces with the help of voice and biometric technology completely enhances the security measures. Thus, the author also recommends installing this system to the banks which will only allow the existing consumers into the particular area where all their tasks are being executed.

According to Trianti 2021, face recognition based attendance systems using Flask and Python have been discussed by the author. Where the author states about the complexity of the back-end system which critically involves various technologies rather than the front-end process which is simpler which helps in providing the complete support to the back-end process of the processed data.

Mostly, web technology which is being enabled by the web browser is being used by the front end system which is being connected to the server. However, the research paper which has been published by the author critically states about the back-end systems that are being integrated by the AI and Python in order to take the attendance of the students.

Moreover, PHP and Flask are being used as python cannot be used alone for completing the entire process that can be readily available into the web. Thus the initial findings are being recorded as it took 3.92 seconds for taking an individual attendance which is being followed by the 47 % of CPU usage and 3.5 Byte of RAM usage in order to complete an individual process.

According to Bist 2020, designing of the facial recognition in order to record attendance by the help of AI based technologies has been discussed by the author. Which critically states about the “attendX” which has been completely developed for taking attendance into the platform. That has been created by the “SIGNY advanced Technologies in India” and which aimed to develop the traditional attendance system by the technology based attendance system which helped to maintain the records of each and every individual.

Thus, the applications being completely designed which helps in recognizing the faces of the individuals and majorly focus into the certain three parts of the face such as forehead, eyes and cheekbones. In fact this system is also capable of detecting the persons who are wearing masks. Moreover, the method which is being used is “ResNet” along with the “multi layered feed forward network” to obtain the desired results in a short span of time. Which serves to save a lot of time to the peoples which are involved in carrying forward this certain activity.

According to Uddin 2021, the Ai based facial recognition system which helps in real tracking of the persons has been discussed by the author into his research paper. Which critically states about the importance of the attendance system into the schools, colleges, factories, industries, healthcare sectors, etc. which takes huge time along with the process to make a complete process.

Which are being considered as one of the oldest techniques which uses lots of time as well as energy. In order to overcome this problem FR system is being installed which completely works on the basis of AI and Python for accessing the data of the peoples in order to recognize them and to make them present for a certain period of the day. Moreover, this system is being capable of detecting multiple faces for making of the attendance.

Thus the recognition part is completely done by using the “DLib and ResNet-34” by the help of two separate camera which are being installed in the different direction for noting the arrival as well as the departure time and which are being calculated sequentially to an record margin of about 96.03% of accuracy rates.

According to Huang 2020, the attendance system which is completely based upon the dynamic recognition of the faces has been discussed by the author in his research paper. Where the author states about the video based attendance system which is being designed in a certain way which will be used for the complete purpose of the recognition of the faces in a real time.

However, the author also states about this system which has completely both i.e. “multi-user attendance and face liveness detection” at the same time. Which is also capable of automatically collecting the required data into the served as datasets for making of the effective calculations which are being carried forwarded by the help of huge algorithms which takes part in it. However, the facial detection part is being completely based upon the MTCNN i.e.

“Multitask Convolution Neural Network” algorithm and the facial recognition part is being completely dependent on the “FaceNet” algorithm. In fact the framework which is being followed is tensor flow in which the facial liveness detection is being carried out by the ERT i.e. “Ensemble of Regression Tree” algorithm. Which helps to detect the eye blinks of the users.

Hence, finally the author states about the attendance system which is being written into the Python language and the user interface is being completely designed in the QT library. Furthermost, the results which have been obtained from the experiment resulted in attaining good performances to recognize the people’s faces in real time and which significantly reduced the rates of rejection by 2%.

According to Bhatti 2018, a smart attendance system which helps in managing the overall works by the help of facial recognition has been discussed by the author in his research paper. Which critically states about the huge efforts which are being exhausted in maintaining the attendances which completely depends on the huge variations of the daily assigned works and which is typically being considered as one of the challenging tasks.

However, the conventional or the traditional technologies which are being used for maintaining or keeping of the attendance records were considered to be hectic and which also consumed specific amounts of time and which often led to the manipulation of the data. Thus which created a huge problem for the instructors in maintaining the essential records of the students into their respective classes are being eased by using the modern technology which uses peoples data into the data set of the system and which helps to give proper attendance automatically into the required dataset.

Moreover this system also helps in providing the complete set of data which are being critically recorded into the system i.e. arrival as well as departure into the system and which can also help to provide the complete statistics of the each and every students which are likely or less likely to attend the classes. Thus, the author also stated about the deep learning technologies which has also been used into this entire system in order to generate histograms for analyzing the entire sections of the classes which are being attained by each and every students respectively.

Thus, the author also states about the effectiveness of the entire system which can be used to detect multiple faces at a single time and the data’s are being recorded into the real time as well. Which makes the entire process more feasible for the managing of the entire work which is to be executed in a short period of time.

According to Praveena 2019, Implementing Open-CV and Python for smart attendance monitoring systems has been discussed by the author. Where the author has stated about the importance of the attendance system into an institution that helps in tracking the overall progress as well as performance. However, different types of institutes perform different methods in executing attendances for fulfilling their sole purpose.

Thus in this modern world technology has completely acquired a large portion of the human presence and in which the overall works are being transmitted through the technology which helps in saving the huge time which is being required for taking attendance. Moreover, the author has stated about the facial recognition system which is being exaggerated by Open-CV and Python helps in detecting the facial structure of the human beings and can be easily recognized for the sole purpose of making an fruitful attendance and this can also be used for the noting down of the departure time.

In fact this system helps the instructors as well as students to save a huge amount of time which is being generally wasted while taking offline attendance. Furthermost, the attendance is being recorded through the help of a camera which simultaneously clicks pictures of the students and which also helps in detecting the right person by running the clicked picture into the databases. Which also helps to send follow up messages to the parents and the attendances are being noted down into the attendance sheet.

According to Saifuzzaman 2021, implementation of advance attendance system into a modern education institutions using a computational intelligence has been discussed by the author where the author has stated about the modern age which is intensively engaged to seek information’s from the technologies and are being used to these technology to be a part of their life.

However, the computational intelligence which is being used into the institutions which completely serves to note down the attendances of the students and which also helps in making great analysis of the performances which are being achieved by each and every student. Thus this system of taking attendances automatically involves facial recognition system through the cameras which are being installed into the whole campus and which also helps in noticing down of the arrival as well as departure times.

The main components which makes this process feasible and makes it into a working condition is through the help of coding language i.e. python which is being embedded into a Raspberry Pi 3B+ kit. That is being help to convert the whole image from colorful to grayscale which is being used for the sole purpose to extract the points on the face of a human being which will be used to denote the students in marking them as present. Moreover, this process is also being helped by machine learning which is being used for training of the entire procedure by the complete usage of LBPH face recognizer. [Referred to Appendix 2.0]

According to Nyein 2019, the classroom attendance system of a university by the help of Support Vector Machine and Facenet has been discussed by the author. Where, the author has discussed about the facial recognition system which is a quite popular system which is being used for the complete detection of human beings accurately and the rate of false detection or failure is about 3% which is very low. Thus this system is slow and sometimes it doesn’t functions properly in detecting multiple faces at a single time and also lags in detection of the peoples due to poor resolution.

Thus the main objective which has been illustrated into the research paper which has been published by the author have stated about the accuracy which is being attained by the facial recognition system and which has been combined of two things i.e. SVM (“Support Vector Machine”) and Facenet for attributing great results. However, the system which has been proposed by the author critically discusses about the extension which is being given by the FaceNet of an about 128 dimensions and in which the SVM is being used for classifying the given set of data.

Moreover, the results which have been attained from the university helps in portraying about the propose which is being used for the recognition of the peoples into university helped to detect the faces with 99.6% of accuracy which is being considered as one of the biggest achievements which has been obtained from the proposal and which has also been considered as one of the best method than the VGG16 model.

According to Ghani 2021, the complete development as well as analysis of the software which is completely based upon the machine learning helps in assisting the online class during pandemic has been discussed by the author. Which states about the changes which have been brought by the covid-19 virus. Significantly the author is more focused to opt for the different approaches for completing the syllabus on time so as to make the consistent studying habit by the students through the online teaching modes.

This introduction of the online class have brought huge difficulties as well as huge challenges for the instructors to mark each and every student present so in order evade this description the author have stated of discussing about the computer program which is being developed for recognizing the each and every students and thus this system helps in recognizing the each and every students and which also serves to make the students present for the entire class.

This system consists of the green and red square box which is being used soulfully for the recognition of the facial analysis. Moreover, the author also discusses about the importance which is being served for giving the complete attendance to the students as the program easily recognizes the facial structure of the each and every students as per the databases which has been submitted into the machine. In fact this system also helps in knowing about the exact time in which the students enter as well as exists from the class.

Furthermost, the author has also discussed about the model designing, performance analysis as well as different aspects in which the entire program has been discussed.

According to Chaya Devi 2020, the smarter way of taking attendance through the process of “Image processing and convolution Neural Network” has been discussed by the author. Which completely states about the algorithm which is being used for determining the attendance is completely from image processing and “convolution neural networks” i.e. CNN.

Which helps in playing an important role for the complete development of the entire procedures and on which the entire system is being dependent. Therefore, in order to enhance the overall reliability of the entire system through the ANN i.e. “Artificial Neural Network” along with the image recognition process. Thus, in this process the DL i.e. “Deep Learning” is also being embedded for making an efficient process which will help in completing relying of the entire procedures which will help in benefitting the complete sections of the entire proposal which is being stated by the author.

Moreover, on the other hand the author have also stated about the components which is basically consisted of the three components i.e. face scanning and detection which is being fulfilled by the HAAR cascade method, secondly by training of the ANN and CNN modules and lastly by the complete recognition of the face back system which is being used for the complete upgradation of the entire system which will be helpful for attaining of the complete knowledge as well as upgradation of the entire process which will help to get the required amount of time at an required time being.

In fact in this proposal which is being stated by the author which uses huge technologies only helps in providing the accuracy which is being required for taking of the accurate attendance from the facial recognition system which also helps to serve the overall time being that is being required for optimizing the entire runtime. [Referred to Appendix 3.0]

According to Geva 2021, introduction of a facial recognition system for managing the class attendance has been discussed by the author. Which clearly states about the automation that are being completely delivered from the technologies or the devices which are being connected through the internet. However, the attendance system which is being used for the complete system which helps in transmitting the traditional signature into the entire section which helps to serve the common purpose i.e. recognizing the face to make an effective attendance.

Which also helps in developing a comprehension embedded into the entire class attendance is being used for a system using facial recognition with the help of “Raspberry Pi THARTUNS PASPBIAN (Linux) operating System” which is being embedded into the micro SD card.

That is being placed into the LBP i.e. “Local Binary Patterns algorithms” which is being used for the complete recognition of the faces of the students which are being recognized by the entire system and on which the data are being procured by MySQL databases and which is being connected further to the attendance management system web browser.

That is being used for the making of the entire system more sophisticated by the entire segment by a record margin of 95% accuracy. Furthermost, the author has been used 11 different kinds of people’s images which is being used for the completion of the entire process for batting this accuracy. [Referred to Appendix 4.0]

According to Setialana 2021, the complete intelligent attendance system which uses facial recognition systems by the help of CNN method has been discussed by the author. Which critically discusses about the actual recordings which are being met for recording the attendances in between the lectures are being majorly done by the help of several ways and which is being also used to denote the complete denotation into the entire attendance sheet which helpfully records the entire data of the each and every students.

Thus, it was found that the traditional methods which are being used are less beneficial in solving the entire completeness of the research paper. However, the researcher also stated about the huge researchers which have made the entire processes of taking the complete attendance by the help of RFID technologies as well as QR codes which will help to make an effective as well as efficient methods which will help to bring the completeness of the entire system which will aim to bring the tidal fruitfulness of the entire results.

Moreover, the author has also stated about the ANN as well as CNN which is being embedded into the complete section by the help of deep learning which helps to train the entire system so as to make optimum performances. Furthermost, the author has also stated about the test results which is being obtained from the accuracy of noting down of the attendances effectively from the data which have been collected from the 16 students during an lecture session which helped in getting an optimum accuracy for about 81.25% and which also helped in making of the entire research feasible to work effectively with this entire result to develop an complete procedures. [Referred to Appendix 5.0]

According to Gupta 2020, Usage of OpenCV as a form of automated attendance system has been discussed by the author. Which critically states about the noting down of the attendance in the traditional form and which have also completely restricted in making of the entire process time consuming. In order to reduce the complete wastage of the time which is being required in taking attendance by the introduction of the technology which will ease the time of the instructors to take the attendances digitally by the process of facial recognition.

In fact the major reason which is behind this system is to improve the complete adaptability as well as performance of the attendance system in which the entire procedures is being followed for making of the entire structure of the entire system and which also helps in marking the entire research structure of the performances.

Thus the overall idea is completely based on the procedures which helps in adding as well as helps in completing the entire process of automation. Furthermost, the author has also discussed about the programming language which is being used for the working of the entire process is by the Python. The LBPH model is being used for the recognition of the faces for making attendances and the recordings are being transmitted digitally into the spreadsheets.

According to Khan S 2020, Real time tracking of the attendance among the students by the help of facial recognition which uses API and OpenCV has been discussed by the author. Which critically states about the difficulties along with the huge errors which are being obtained in the form of taking down of the attendance offline which also takes a huge amount of time has been discussed by the author.

Thus, the problems which are being raised through the biometric machines is touching the devices during the pandemic have been restricted and which also makes a long queue in making attendances. The facial recognition system helps in detecting the students through the help of cameras and which also helps in noting down of the arrival as well as departure time of each and every student. However, the author has also discussed about the algorithms which helps in detection of the objects like BPNN i.e.

Back Propagation Neural Network, RCNN i.e. “Region based Convolutional network” and the single shot detector which is being used for the sole purpose of the YOLO V3 platform which critically demonstrates about the entire process by you only look once and which is also being carried forwarded by the API which uses “Microsoft Azure” for the recognition of the face. Which is being effectively used for noting down of the arrival as well as departure times.

Furthermost, this system which is being prescribed by the author works effectively in order to denote the efficiency which is being achieved for the sole purpose of gaining higher accuracy in order to detect the facial structures of the students as well as noting down their attendance.

According to Castillo 2018, generation of class attendance through the help of multiple facial detection systems which uses ANN has been discussed by the author. Which critically states about the proper implementation of the facial detection system which will help to recognize the people through the algorithm which is being used is ANN i.e.

“Artificial Neural Networks” by a system of video recordings as an input median. However, this system is being used for the detection as well as generation of the class attendance reports which ensures the instructors that the student is being present or absent. This also helps in knotting down of the students which arrives late into the class. Moreover, the test module which is being presented by the author states about the proper utilization of the raspberry pi module which helps in building communicating with the pi camera that is being also used for taking the entire video of the class.

Furthermost, the author also stated about the confusion matrix which is being used for visualizing as well as measuring of the performances which is being associated with the algorithms and classifier. Which helped in attaining the optimum accuracy for about 87.78%.

According to Pandimurugan 2020, face recognition systems which use IOT and smart applications like machine learning have been discussed by the author in his research paper. Where the author has discussed about the use of IOT i.e. “Internet of Things” which has already captured as well as gained a huge popularity among the human beings in order to derive the usefulness of the products which are not being developed properly.

Thus the combination of machine learning and artificial learning helps in making the entire process useful which critically is having the useful donations that are being procured into the system so as to give a better outcome. However, this helps in memorizing the faces of the human beings and which will give a complete coverage of knotting down of the attendances which will be beneficial for the system in a timely manner.

Moreover, the project which has been illustrated about the author states about the complete governance of the databases which helps to make the complete recognition of the problem which are being faced down by the researchers in order to take down the complete attendance through this entire system. Furthermore, the author also states that this technology can be also used in different areas such as airports, railways, offices, etc. and which can be further developed.

According to Arsenovic 2017, a facial recognition system which helps in taking attendance and which is based on deep learning has been discussed by the author. Which clearly states about the interests as well as accomplishments which has been done into the areas of the CNN i.e.

“Convolution Neural Networks” in the determination of the face which will help in giving the correct attendance. However, the author has also discussed the different kinds of steps which have been taken to fulfill these test results in his research paper along with the advanced techniques.

Moreover, the primary aim for this research paper was to develop a state of the art facility which will be basically based on the purpose of deep learning into smaller datasets. Thus the results which have been obtained when the tests have been conducted were having an accuracy for about 95% from the small amounts of data.

Furthermore, the author has also suggested implementing this system with similar other systems so as to enhance the over functionality of this system.

According to Akay 2020, the automated system which uses facial recognition for making attendance has been discussed by the author in his research paper. Which critically states about the rise of technologies which helped in easing human life. Thus the research paper also contradicts with the different kinds of algorithms as well as software’s which are being required to make a complete automated system which have reduced the complete interventions of the instructors.

Which also helped in saving huge time that is being required by the instructors as well as students in taking attendance. Thus, there are huge advancements into this technology, two different kinds of face detection algorithms have been discussed which are “Histogram of Oriented Gradients and Haar-Cascade”, which also helped in making a concise comparison to understand about the data. However, the deep learning terminology which is completely based into the CNN i.e.

“Convolution Neural Networks” has been deployed for the complete understanding as well as identification of the students in their respective classrooms. Moreover, a checking feature has also been included as a measure which helped to make a safer distance at the time of covid-19 virus. Furthermore, a “graphical user interface” i.e.

GUI that has been completely designed by python has also been implemented into this system which helps in making of the fruitful results by detecting the students at a very less amount of time.

According to Chen 2021, the use of “artificial intelligence and robotics application development” which is being used for supporting technologies has been discussed by the author. Which critically states about the rapid growth of devices which are being completely overtaken by the technologies.

This paper which is being published by the author completely states about the automation, which is being brought by the different kinds of algorithms and technologies in making the entire process feasible while taking off the attendances of the students.

However, the author has also discussed about the great accuracy that has been attained from the making of the students’ attendances automatically. Taking attendance in a traditional way is occurring with many challenges as well as time which is being replaced by the technologies and which is being plotted by the help of huge algorithms as well as programming languages.

Moreover, this advanced system is being embedded with the help of excel sheets which helps to note down the students attendance for their respective classes, which also marks the students which arrive late into the class by the term late comers. Thus, this process helped the instructor as well as the institutions in overcoming of the difficulties as well as which also helped in uplifting the behavior of the students in a timely manner.

According to James 2019, monitoring of the students by the help of facial recognition systems into a school bus has been discussed by the author in his respective paper. Where, the author has clearly stated about the rise of crimes which are occurring and the school buses are being targeted as a victim.

That mainly occurs due to the daily commute from their respected homes to the school or vice versa. Most of the cases it has been found that the students are being completely accused of the social crimes which critically includes sexual harassments that are being occurred into the bus and which has been considered as one of the most concerned situation.

So as to reduce or to completely evade these kinds of disruptions or the malpractices the “real time monitoring system which uses image processing techniques” has been implemented into the respective buses. This process is completely run by the help of cameras which helps in identifying the respective students of the schools that uses the buses to ply into their respective homes and which also helps in monitoring of the movements which are being done the respective students inside the buses.

However, this system also helps in recognition of the facial structure of the students and their facial count are also been done. Moreover, this system is being equipped with an alarming technique which helps to inform the localities as well as this also helps in sending a quick SMS to the nearby police stations along with the school authorities. In fact the technologies which are being used into this system like OpenCV i.e. Open computer-Vision library and which is being completely implemented by the help of python.

Furthermore, the facial detection is being done by the HAAR-Cascades Classifier. Which completely helped in reduction of the sexual assaults which were faced earlier.

According to Khuran 2021, Biometric facial detection system which uses AI for making a successful attendance into an organization has been discussed by the author. Which critically states about the critical process that is being used for the managing of the organization’s value and which have also helped the institution in deriving quality as well as timely attendances. In fact the biometric process sometimes helps in denoting the false attendances due to malfunctioning of the sensors.

In place of replacing the older versions of technologies which are being used into this complete set of model helps in producing of the efficient results through the technological framework which is being used for making as well as approaching the attendances. However, the facial recognition system is being used for the sole purpose of upgrading of the attendance marking system through the process of AI and ML with the complete help of LBPH algorithm which is being used for providing of the clear and precise output.

Moreover, this LBPH algorithm is being completely used for the processing of the pixels into an image which has been detected for determining of the threshold values along with the binary pattern. In fact a regular database has been created for the managing as well as preserving of the overall attendances which is going to be executed to manage as well as preserve the future proceedings which is being used for the crucial execution of the entire system that is being used for the complete reduction of the false as well as negative responses into an biometric attendance management system.

According to Patel 2018, smart attendance system on the basis of facial recognition which uses IOT has been discussed by the author. Which critically states about the importance of attendance which is being one of the compulsory requirements that is being needed by most of the organization. Thus, maintaining and keeping a complete track on to the traditional offline methods which took huge time as well as efforts sometimes lead to mistakes.

In fact there are huge devices which are available in this modern world to make an efficient attendance system like biometric, eye detection, RFID, voice recognition, etc. thus, the research paper which is being published by the author completely states about making a permanent solution and a device which will help to make precise attendances by identifying the facial structure of the human beings and which will also be responsible for noting down of the specified real time beings on which the persons performs its daily activities.

Thus the system which is being discussed in the research paper states about the initialization measures which are being taken by the system if some persons are absent. It helps in sending the follow up email with a standardized SMS which will be delivered to the concerned authority of the organization and it will also be delivered to the parents. Moreover, the author also states about the objectives which will help in making of the innovations into existing projects which will also add some features like storing of the data in an economical way.

According to Akbar 2018, RFID attendance system and face recognition attendance system has been discussed by the author. Which critically states about the complexity which is being found while taking attendance in a pen and paper mode. Which also have raised concerns while maintaining the proper attendance system. Thus, in order to reduce this huge problems which are being faced while executing attendance manually a fully automatic system which will help to reduce the manual efforts has been discussed by the author.

However, the model completely focuses on facial recognition system which is being completely incorporated with the RFID i.e. “Radio Frequency Identification”, which will help to detect each and every students whose database has been uploaded into the system and which will help to make an automated attendance system. Moreover, this system also helps in procuring the data i.e. arrival as well as departure times of every students.

Significantly, this system also makes a list of students which arrives late to their respective classes. Furthermore, this system is carried by the presentation of activity logs into the system which also helps in tracking down the activities which are being performed by each and every student. In fact the two functions work simultaneously which are facial recognition that helps in recognizing the face and RFID which helps in verifying the persons or students.

Hence, the author has also mentioned about the added feature which is being implemented into this model i.e. IR, which will help to turn on all the electrical equipment which are being present into the room in the presence of a student and simultaneously it will also help to switch off all the electrical items in the absence of persons or students.

According to Kakarla 2020, use of CNN in making of the smart attendance management system has been discussed by the author. Which critically states about the role of “Convolutional Neural Networks” i.e. CNN, which plays a vital role huge applications such as proper detection of objects, surveillance, tracking down of objects, etc.

Thus, huge researchers has been conducted by the help of CNN which states about the surveillance system can be used for recognizing the facial structure of the human beings into an organization for maintaining the attendance records into an smarter way and which will also help in delivering the essential things which is being used for the determining of the performances. Moreover, the results which have been obtained by using this system helped in achieving the complete attendance accuracy for about 99%.

Furthermore, the system is being induced into the web based model for easy access into the real time and which is also easy to maintain as well as deploy.

According to Othman 2019, preventing fake attendance by the help of Deep learning approaches into a smart school has been discussed by the author. Which critically states about the importance of deep learning which has been a hot as well as popularized topic which will be help to upgrade the human life by initializing it into devices.

However, this paper consists of this crucial discussion of technological upgradation which helps in making of proper attendance system which will help to make proper and concise attendance without any disruptions. This is being done by facial recognition which is being carried out by technologies like deep and machine learning. Moreover, the system which is being used for making a precise attendance is being embedded into the platform which is quite efficient.

Thus which is being carried out by the facial databases which allows in specified classes in determining fake attendances. In the recent survey it has been found that the students are tended to fake their friends attendances if they are absent for a day and which is to be stopped for making a better environment.

Thus the author also helped in stating about the taking attendance during an examination which will help to detect the student’s arrivals as well as departures into the smartphone of instructor which is being completely based on to the platform such as IOT and which also lowers the overall costs which is being required in this system.

According to Arya 2020, use of CNN into a smart attendance system has been discussed by the author. Which completely states about the traditional ways in taking down of attendance into an organization often leads to the manipulation of numbers as well as figures which are being used for heavy disruptions. However, the author also states about the modern technologies which is being used for automatically detecting the persons which belong to a particular organization by detecting the facial structure by the help of cameras which is being transmitted through the CNN.

Moreover, the author also suggests to use or to install this system into the offices or academic institutions for detecting the persons and noting down their attendances in arrival as well as departure times. In fact the author also states about the “Eigen faces and Fisher faces” which are completely sensitive to lightning, noise, obstruction, illumination, posture, etc.

hence, the CNN is being used for making an accurate attendance records into the excel sheets as well as to that of the web browsers. Therefore, the test run results which have been obtained showcases about the 99% of accuracy. In fact the author also suggested of using MongoDB as a backend database for maintaining records.

According to Kuang 2020, real time tracking of the attendance through the use of deep learning algorithms has been discussed by the author in his respective research paper. Which critically states about the importance of attendance into a workplace or into an academic institution for determining the regularity of the persons. Thus the offline method which is also termed as traditional methods are having huge problems which leads to manipulation of the data in order to make an effective activity log.

However, the author has presented a detailed insight about the facial recognition system which is being used for the easy detection of the persons who belong to the same organization and which helps in procuring of the data to make an effective activity log. Moreover, the author has used a pre-trained “HAAR Cascade model” for the easy determination of the facial structure of the persons by the help of cameras which also helps to track the motions of an individual person.

Hence, the trail test results which has been obtained shows that the system is quite efficient enough in taking down the arrival as well as departures of the students into an particular organization for a time being has been discussed. In fact the author has also launched a survey which will help to investigate the pros along with the cons into a particular system.

2.3 Theories and Models.

This section helps to understand about the different kinds of theories along with the models which have been used by researchers in their research work for making an efficient system which will help to determine the attendance area from the implementation of CNN along with the ML and DL. Which helps to self-train the system in equipping the modern technologies which will be used for the making of the process simple (Guleria and Sood 2018).

Thus, this system helps to give the relevant frameworks on which the performances of the individual peoples can be graphically monitored and which also allows to real track a particular person into an organization. However, this system showed a great response to its trail and which has attained 99% of accuracy in determining correct peoples. Hence, this system also allows sending a follow up email along with the SMS to the respected guardians if the student is being absent for a particular class.

Thus the other model which also showed a great positivity in determining the attendance through the facial recognition which is being completely operated through the help of “Raspberry Pi THARTUNS PASPBIAN (Linux) operating System” which is being embedded into the micro SD card. Which is being placed into the “Local Binary Patterns” algorithms in determining the face and which is being completely procured by the MySQL databases that are being connected through the management of attendance into the web browser.

Which helped in making of the 95% of accuracy and which also helped in denoting graphs which showcases about the behavioral patterns into particular human beings.

In fact there are huge models along with the theories which have been used by the researchers in their research work to make the process simple while taking attendance and attain huge positive results (Muttaqin and Nopendri 2020).

The barcode scanning system was prone to get damaged easily and the overall accuracy received was 50%, RFID model is prone to misinterpreted data as the signals are being affected by metals and liquids and the accuracy received from this model is 70%, NFC is highly expensive and requires huge maintenance in which the accuracy received is 65% and the model CNN which is embedded with AI, ML and DL takes a significant amount of time in order to train and which is being considered as one time investment and helps in giving an accuracy of 99%.

2.4 Literature Gap.

In the empirical study section huge studies which have been published by the authors globally have been discussed which completely portrayed the effectiveness of facial recognition systems for noting down of the attendances effectively. However, the researchers haven’t discussed about the huge costs which are being associated to implement this system in order to maintain a steady track record of the persons into an institutions or to the organizations.

However, this system requires a huge room where the backup of power supply is being transmitted throughout the entire system which enables this system to work 24*7. Thus the costs also arises from maintaining of the system which critically includes CCTV cameras for surveillance, servers, sensors etc. which requires timely maintenance (Jadhav et al. 2021).

In fact the other drawback which has been observed that the researchers haven’t discussed about the motion detection cameras which will operate only by attaining the signals or the motions from the peoples, which would significantly help to reduce the overall electricity as well as data storage costs.

On the other hand this system i.e. AI based facial recognition in the determination of attendance into an organization fails or misinterprets the overall data acquiring process as this system cannot be used in the huge crowded places where constant motion takes place.

The Use of embedded systems such as Raspberry Pi, Arduino and others are being used as well as incorporated easily for taking up of the actual benefits of the overall system which will also help to increase the overall security system. Which has been recommended to use in each and every system so as to ensure complete safety as well as security of the organization.

2.5 Conceptual Framework.

Figure 2: Conceptual Framework. (Source: Self-created in draw.io)

D.V i.e. “Dependent Variables”:

Support Vector Machine – It is being used into the system where it helps in recognizing the facial structure of the human beings and in which the rate of failures in detecting the face has been composed to 3% which is very less while compared to the other models which has been used by the different researchers (Fung-Lung et al. 2019). Thus, using this system it helps in attaining of the overall rate of facial detection is about 99.6%. Which helps in detecting the accurate face.

RFID i.e. “Radio Frequency Identification” – It helps in taking of the correct attendance which is being completely embedded with the ANN and CNN and which is being executed by the help of QR codes. It basically senses the frequency of the individuals in order to make a correct assumptions in taking attendance.

Raspberry Pi – Some researchers have introduced Raspberry Pi into the facial recognition system to enhance the overall security of the organizations or the institutions where this system is being introduced. Which will warn the organizations if some un-authorized personnel’s enters into the premises (Khairnar and Khairnar 2021). This is being embedded into an SD card and which is being placed into the LBP i.e.

“Local Binary Patterns Algorithms”. That helps in production of the data in MySQL which is being further connected to the attendance management system which can be run into a web browser for easy monitoring purposes. This process helps in attaining 95% of accuracy in detecting the correct face.

CNN i.e. “convolution Neural Network” – This is mainly run by the help of AI, ML and DL.. Which also helps in providing the adequate amount of training which is being required for easy training as well as enhancement of procedures. Moreover this process is also being fulfilled by the help of HAAR cascade method. That helps in accurately determining the face and making of the entire attendance.

I.V i.e. “Independent Variable”:

Facial Recognition System: It has been considered as one of the independent variables as this system can be fulfilled by the huge technologies or the devices which are being available in attaining of the huge accuracy (Giovani et al. 2021). The devices or the systems which are dependent on this has been discussed in the above part.

2.6 Conclusion.

This is the summarization part of the entire section i.e. literature review. Which helped to give a complete knowledge about the research works which has been conducted into this segment bby the help of huge technologies as well as devices to make an efficient result. Which will also help to determine the percentages of results which were conducted into the areas of research works.

Thus the empirical study section helps in giving a complete weightage into this research paper by providing the reviews of the published papers from secondary sources. However, this chapter also consisted of the theories and models, where the most prominent theories which helped in fulfilling the desired results have been discussed.

Moreover, this chapter also consisted of the conceptual framework, where the independent as well as dependent variables have been discussed. In fact this chapter will give a clear and concise idea, understanding as well as knowledge to the readers in knowing about the functionality as well as importance of using this system into an organization.

3.0 Methodology.

3.1 Introduction.

This chapter i.e. methodology helps in describing about the entire procedures which are being used for conducting the research work. Thus, this chapter helps in demonstrating about the different types of procedures or techniques that have been used while performing this entire research work. However this chapter helps in outlining the different stages which are being used majorly for proper planning of research methods that will completely help for the huge implementation of different processes.

Which are being used for the huge collection of data, conducting analysis and obtaining the results. Hence, this chapter also helps in covering the overall works that have been conducted into the entire research paper. In fact this chapter will also help in providing a clear and concise overview of research philosophy, designs, methods, approaches and strategies. Finally, this chapter will help in providing the necessary details about the data collection methods, analysis, ethical consideration, limitations along with the time horizon which showcases about the entire time which has been required for the conduction of this entire research work.

3.2 Method Outline.

This section i.e. method outline in the methodological chapter helps to determine about the systematic procedures which are being followed from planning’s to obtaining of the results that are being obtained and are completely based on the objectives that is established. Thus, the planning phase of the overall research work helps in identifying as well as determining of the various strategies as well as approaches which have been undertaken to reach the desired goals along with the objectives in order to fulfill the entire research work (Nande et al. 2017).

However, the data collection process has been initiated by the help of suitable strategies, approaches and methods. Which has been also considered as one of the most crucial stages and which accounts for the overall information that has been obtained from the findings and results.

Moreover, the data which has been collected from the particular sources are being analyzed by the help of appropriate techniques which helps in identifying the researcher and which has been also used to make hypothetical statements that has been used for the development of the entire research work. In fact, this section helps in indicating the five crucial stages into the entire research procedures, which critically involves identification of appropriate methods, planning’s, strategies, approaches and resources in order to obtain the results.

3.3 Research Philosophy.

The overall research work which is being conducted is typically based on different types of philosophies, which helps in attaining of the strategic objectives which are being completely based on the purpose along with the problems (Kurniawan and Zaky 2020). Moreover, proper identification of the research philosophy helps in determining the other crucial aspects which are being required into this entire section of the research work such as approaches, methods and strategies.

On the other hand different types or kinds of philosophies are also being used for conducting a fruitful research work. Generally the types of research philosophies critically include “realism, positivism, pragmatism and constructivism”.

This research paper follows “Realism Research Philosophy”, as this research paper completely relies on the different kinds of ideas along with the approaches which have been portrayed by the different researchers globally which critically helps in assuming the complete scientific approaches for the development of knowledge. In fact this theory has been divided into two broad categories i.e.

direct and critical realism (Jaiswal et al. 2022). Which also helps in giving of the complete explanations from the complete analysis that is being used for giving a complete explanation of the quantitative theories. Which also emphasizes the research work that has been used into the specific areas in order to make proper and concise decisions thus which are also intrinsic in nature.

3.4 Research Approach.

There is a huge availability of research approaches that can be used for conducting a great research work as well as to conduct a data analysis. Hence, most of the research paper consists of two most important research approaches i.e. “deductive research approach and inductive research approach”. Thus the deductive research approach helps to focus on obtaining of the results and are being completely based on that of the prominent existing theories.

While on the other hand the inductive research approach helps in portraying about the results along with the new theories which are being derived from a complete new set of information (Mishkat et al. 2019). Thus the choice of selecting a research approach completely depends upon the research philosophy. Thus the research philosophy which has been used in this entire part of the research work is “realism research philosophy”, which helps in emphasizing to opt for the deductive approach.

Moreover, the deductive approach helps in highlighting the overall research work which has been carried out on the basis of knowledge as well as to that of the information’s that has been gathered both from the primary as well as secondary sources. Furthermore, this also allows for making a strategic analysis in order to generate potential results which helps in discussing about the further stages. In fact, the deductive approach will be helpful for conducting the most appropriate approach in this entire research study.

3.5 Research Design.

There are two different kinds of research designs present, that are being used mostly or widely by the researchers globally such as “Qualitative Research Design and Quantitative Research Design”.

The “Qualitative research design” majorly focuses on the perceptions as well as observations in order to analyze the entire set of resources from the given set of data. Moreover, it is being concerned with establishing strong answers to the respective questions such as how’s and why.

Which is also being defined to be subjective rather than objective, in which the overall findings are being gathered in the form of numerical. Which means that the data which has been collected or being gathered in this method cannot be used for analyzing in a quantifiable way as there are no such commonalities found in between the various types of findings.

The “qualitative research design” in a research project helps in determining quantitative research methods. The overall design varies with the type of methods that are being used and the data are being collected from the secondary sources like telephonic interviews, online surveys, face to face interviews, surveys, scholarly articles, newspaper articles, websites, blogs, etc.

However, in this type of research design it is being aimed to discover the psychology of the human beings which critically determines the behavioral changes or the actual feelings in a specified way (Wu 2019). Moreover, it is being involved with huge sample sizes which is being used for concentrating on the overall quantity of the responses which is being helped to gain more focus and which also helps in gaining quality research.

In this research study the entire research paper has been conducted critically following “Quantitative research design” (Dolas et al. 2021). This design helps in attaining the crucial information which are being required for completing the entire research work in a systematic way, where all the details along with the data are represented in the form of experiments. Thus, in other words it can be portrayed as experimental research design. Which is basically a scientific approach that completely uses two complete sets of variables.

Figure 3: Experimental research design. (Source: researchnet.net)

3.6 Research Strategy.

In this section i.e. research strategy it helps in discussing as well as explaining about the techniques, which are being used by the researcher in order to reach out to the participants in order to collect the relevant information into the areas of research work. However, a single study can be used for collecting multiple strategies in collecting as well as analyzing a wide range of data. Which are being pretended for complete development that is being obtained from the desired outcomes.

Which is generally carried out in a step by step planning’s in which the overall actions are being given to direct the overall efforts as well as thoughts in order to conduct the research work systematically in a scheduled work which will help to produce quality results along with the detailed reporting. Moreover, selection of strategies is being completely dependent on the philosophy of the entire research work on which the research has been adopted into the overall research study.

The study which is being used in this research paper is realism philosophy which relies on the different kinds of ideas along with the approaches which have been portrayed by the different researchers globally which critically helps in assuming the complete scientific approaches for the development of knowledge (Muthumari et al. 2022).

Furthermore, the most prominent way in which the essential details along with the data which has been represented into this entire research paper consisted from the primary sources along with the secondary sources. Thus the primary sources include surveys that lead towards the raising of the questionnaires and in secondary sources the data are being collected from scholarly articles, newspaper articles, blogs, websites, books, etc. through the process of case study.

Figure 4: Research Strategy. (Source: Self-created in draw.io)

3.7 Research Method.

There are three different kinds of research methods, in which a particular type is being selected for executing the overall research work. Thus the names of the different kinds of research methods are “mono-method, mixed-method and multi-method”. This research paper is completely based on the AI based smart attendance system which uses facial recognition and is being run by python for executing the overall work in order to get the desired results uses huge “quantitative research analysis” to give a brief information about the previous research works which has been created by the researchers.

The research method which will be suitable in this entire research work will be carried out by the help of mono method (Bairagi et al. 2021). Which typically means that only one type of research method is being followed in order to gain the complete information that is feasible to execute this entire research work. In fact which helps to imply a great opinion which will be used for the conduction of this entire research work where the results which were obtained are being placed in a systematic way which will help the reader to understand about the entire research work that has been conducted.

3.8 Data collection method.

This section i.e. data collection method helps to understand about the entire procedures in which the overall data along with the information which has been required for fulfilling the entire research work has been discussed into the different sections (Lafuente et al. 2021). This research paper consists of both primary as well as secondary data collection methods.

The primary data has been collected in order to perform an experimental design by integrating Python programming language in order to check the effectiveness of the sensor-based human activity recognition sensor in order to determine the health of the human beings, etc. However, a secondary research method is being critically defined as one of the desk research jobs in which the data are being procured beforehand. In which the existing data are being collected as well as summarized to increase the overall effectiveness of the entire research work.

It also includes reviews of published research materials along with the researched results which have been performed by the researchers globally in order to understand the overall magnitude of the entire research work which has been carried out and significantly which will also help to increase the effectiveness of this entire research work (Handaga et al. 2019).

This method is also being considered as economical as it helps to reduce the overall costs which is being required in executing or procurement of the data and the excess amount of time which is also been taken for the conduction of the entire research work. Thus this research paper consists of the secondary data collection method which helps to increase the effectiveness of the paper on which the research has been created and which will also help the readers as well as the new researchers to develop the modern concepts which will be beneficial for executing the entire research work.

3.9 Research Ethics.

This research paper consists of different chapters in which critical explanation of the chapter has been discussed into the different sub sections, which have followed the research ethics. That are being required for uplifting the moral value of this paper. The types of research ethics which has been followed into this entire section is being discussed below in the form of bullet points and which are as follows

Honesty: The data, methods, procedures, results, etc. which have been used in all segments of the manufactured research work or data are not denying false.
Objectivity: This paper does not provide any comments or false models, theories, analysis used to describe has been performed completely according to the requirements of segment response and also ensures Preserve the final result (Rao et al. 2021). In addition, readers will not find any kind of bias working in any part to create an indispensable part that will be widely accepted by readers to get a perfect knowledge of the topic on which research was
Integrity: The data or statements presented in different parts of this study help maintain the promises of the desired results obtained and the following are described with sincerity. What has helped to strive for binding and actions to be resolved.
Carefulness: This study was conducted with priorities by receiving support for secondary data sources. This has helped learn errors or errors while placing relevant information added a weight throughout the section of the Therefore, an appropriate exam has been used to implement this research set.
Openness: The data, ideas, results, tools, resources, etc. which have been used to complete this research paper are ready to be shared with the researchers along with the readers in order to help them by giving the relevant ideas which have been carried out in the entire section of this research paper (Tiwari et al. 2018). Thus it is being also accepted to use the new ideas along with the research activities which are being required for enhancing the overall importance of this research paper.
Respect for intellectual property: This research paper has not listed any data, statements, models, theories, etc. into the entire segments of the research paper without permission. Moreover, the credits have been given to the respective authors from where the data have been collected in the form of in texting. Thus full respect has been given to the respective authors which consists of honor patents, copyrights, etc.
Confidentiality: this research paper has also helped in protecting the confidential communications like grants or papers which are being used for the submission of the publication. That also critically includes personnel records, patented records and military secrets.
Responsible Mentoring: This research paper which has been used for the complete purpose of educating, mentoring as well as giving crucial advice to the students in order to provide their welfare and which will simultaneously help them to enhance their overall knowledge and that will allow them to make their own decisions.
Social responsibility: This paper helps to strive as well as to promote social goodness which also prevents social harms while conducting the overall research work.
Non-Discrimination: This research paper haven’t used any statements or words which will rise to make conflicts among the society or the community (Salazar et al. 2020). Moreover, this paper has also given equal importance on the basis of race, sex, ethnicity, etc. and which equal importance has been given to the colleagues as well as the researchers which helped in conducting this research work.
Competence: this research paper helped in maintaining as well as which also helped in improving the professional competence. Which is being used for educating the mass of the readers in order to increase the overall competence.
Legality: This research paper completely followed as well as obeyed the rules and regulations which are enforced by the local government in practicing the research work and which does not consist of the legal crimes.
Animal care: this research paper has not shown proper respect to the plants and animals. In fact no damages or harms have been caused to the animals.
Human subject protection: This research paper hasn’t caused any harm to human beings. In fact it helped to enhance the human knowledge of the human beings which are being required for respecting their dignity, autonomy and privacy.

Figure 5: Research Ethics. (Source: Self-Created in draw.io)

3.10 Research Limitations.

This research paper consists of effective strategies along with the useful techniques which helped to provide the essential requirements which are feasible for executing the overall knowledge that will help the readers to understand. The major limitation which has been found in this research paper is from the empirical study section where the reviews of the different research papers does not consist of the critical discussion of the security purposes which will be given by the implementation of the further devices like Raspberry Pi and Arduino (El Gourari et al. 2021).

That will help to act like a sensor which will help to provide the concerned feedback of the unauthorized persons who are trying to enter the premises of the organization. If this can be implemented into the system then it will help the organization or the institutions to make a positive result in uplifting their potential. Thus this acts as a limitation which has been observed in the entire research paper.

3.11 Time Horizon.

Figure 6: Gantt chart. (Source: Project Libra)

3.12 Conclusion.

This is the summarization part of the methodology chapter. Which helped in discussion about the concerned parts that has been also considered as one of the vital parts in this research paper and their importance’s has also been discussed. This chapter discussed about the realism research philosophy which is being used in successfully conducting this research paper, it also discussed about the use of deductive research approach which has been used in this research paper, thus this paper also discussed about the quantitative research approach which has been used in attaining the information’s as well as knowledge’s to give weightage in this entire research paper.

Which is being followed by research study which uses case study, mono-research method that is being followed by the secondary data collection methods, ethics which is being used in the overall research work, research limitations and the time horizon which shows about the exact time duration which took to complete this overall research paper.

4.0 Findings and Analysis

Introduction

The section discusses and explains the development and implementation of a real time AI based student’s attendance system which has been developed for taking the automatic attendance in the institution. The traditional way of taking attendance requires a lot of effort and requires huge paperwork’s which is not easy to organize and there is always a probability of a wrong record written in the attendance register and also add to the cost.

The best and alternative way of taking attendance which provides high security and accuracy and not at all required to maintain it. Development of the Attendance system is done using the python programming language and its various modules and for using the library C++ library has been required for running the program as the dependencies were installed in the project environment. Choosing the primary language as python for the development of AI based attendance system has its own benefits e.g., easy implementation of the python, support various tools and libraries for the development environment.

Python provides a high level of scalability of the software and makes it future proof, so that further enhancement can be done without altering a lot of changes in the coding part. The section also does the brief analysis on the various findings while development of the software and the discussion has been done on various topics and arguments has been provided for choosing the method to implement the program in a specific way.

Finding Analysis

For the development of AI based attendance systems a lot of research has been carried out. The resources collected from various sources like journals, books, online articles etc. Research papers that have gathered from various locations have been evaluated and then sorted are from the various journals.

At first it was very difficult to find out the topic related to the attendance system as there were very few articles present about the implementation of AI systems for the recording of attendance (Dalwadi et al. 2020). Doing evolution of various topics of the implementation of the system it has been found that using facial recognition they will be easy to implement the system for taking attendance.

The camera can be used as an input for the program then the program will be design at that way it will analyze the facial structure of specific person and match with the database that is previously stored in the system and the model which will be design for detecting the values and write a record for the person attendance with its name and its time of arrival in the institution. Entry and exit can be recorded in an excel file which holds all the values for the attendance and that can be later easily analyzed and record all the attendance for the students.

The deep learning method will be applied for recognizing the faces. Various problem can be solved using the method like the working principle of a civilization is that first of all it’s look at the source picture for the peoples in the same frame secondly it focuses on each face and with the help of deep learning it try to understand the faces even in face and different direction and also tune the bad lighting.

In this condition also the model will be able to detect the face and record the attendance for it (PauL et al. 2021). The model pics of the unique feature that is available for the face data and utilizes it to tell the variation between the peoples for example the size of the eye how the face structure etc.

Implementation

Software has been developed using the various features of the “Python programming language “ and has been coded in the same programming language. Python has been used for its various functionality and features with its libraries it is easy to implement the software (Ghosh et al. 2021). The libraries which have been used in the programming are cv2 and the numpy and the library which is used is face recognition, os library for listing the path of the directory and the last library which has been used is that time for recording the date of the attendance.

Numpy library has been used for its various mathematical functions which provides in generating random numbers and also helps in calculating the linear algebra and various transformation can be done using this library (Mishkat et al. 2019). Generally it is used for calculating mathematical operations, as arrays have been used in programming the library is very useful for analyzing and providing various calculations with efficiency.

The CV2 plays an important role in the development of this application which is based in artificial intelligence. It provides various functions for capturing the image whether it is still image or a large stream video using the CV2 library it provides various functions and parameters for image evaluation which is very useful for analysis of a video it can be installed using the commands.

This library is fully responsible for the capturing image which will be later used for the facial recognition and recording of the attendance. It is an official build for the python which generally belongs to the open CV but it has various communicative steps which is built from the scratch and with all the errors which were previously in the openCv. The face recognition library of python is helpful in detecting the various features of a face using its inbuilt methods and functions.

Using the features of recognizing and manipulating faces with has been given in food by the camera (Salim et al. 2018). This library has the simplest functions of which is built in Dlib. The library has been evaluated by various developers all around the World and it has been proved as one of the best and highest accuracy, which provide the accuracy of around 99.38% in the benchmark. It contains the various commands and functions which can be imported in the attendance system for detecting the facial features. The Other Dependencies which need to be installed on the project Environment which are listed below.

Dependencies Required

Visual Studio with C++ desktop development (As required for installing dlib library)
cmake
Dlib
Face_recognition
Numpy
openCV-Python

These are all the dependencies which are required for the implementation and development of the attendance system which will record the attendance in real time by capturing the video and detecting the face.

For Writing the Code of and development of software aunty IDE can be used but for this project Pycharm has been used (Guleria et al. 2018). As it is, tools are very user friendly and the implementation and support for the various libraries of python is very flexible.

The code implementation can be understood with its snips attached below and the reason for using this is explained. After all the dependencies installed in Pycharm a test code written as simple.py to check if the model is working or not, or able to detect the image. A temporary Dataset has been used in which some image has been downloaded from the internet for checking the accuracy of the face recognizing model.

Figure 7: Importing Libraries (Source: Self-Created in Pyharm)

In the above image it can be seen that the libraries have been imported, a variable called imageMark has been initialized with the file location of the image that is going to be tested on the model. The image is then converted in RGB format for the accuracy of detection and in the next line the other image of the same person is loaded for detection and conversion is also done in the test image.

Figure 8: Calculating Face Location (Source: Self-Created in Pycharm)

Face recognition module evolves the structure of the face and calculates various values for possible outcomes, and for this face location also specify a unique calculation that shows the values for face location and the location differs for each persons and for matching it also detects the location.

Figure 9: Results (Source: Self-Created in Pycharm)

In the above code the result variable is declared which is initialized with the results of the compared faces and then it prints out the results and the two window pops up showing the results as can be seen below.

Figure 10: Output Results (Source: Self-Created in Pycharm)

As it can be seen the results for comparing two faces using the model can detect the face and the result is found out to be true. After that the same image has been compared with the other person’s face the model shows the force value and it can be concluded that the system is working properly as expected as also it can be seen in the below output where the images not did not match. So the program can be a further step for detecting the real time uses using the webcam can be implemented the same way this program is implemented.

Figure 11: Output Results of different person (Source: Self-Created in Pycharm)

Figure 12: database path (Source: Self-Created in Pycharm)

Alike the previous implementation of simple program the main program assigned with the database but this time the image is not used instead the directory has been directly initialized as dbpath which lists the all files containing the directory (Kandjimi et al. 2021). The function that is initiated lists all images in the directory and the name of the person is given to the file name as well for easily identifying the person.

Figure 13: Converting the Images (Source: Self-Created in Pycharm)

The fetch encoding has been defined for converting the images in the database to RGB from the bgr format and assigned to the encoded variable which consist of the face ignition for the images that has been listed in the database and the return statement is provided for the encoded list.

Figure 14: Attendance function (Source: Self-Created in Pycharm)

The function is defined for writing the record of the student who will enter the organization or institute it will read the face of the data and match with the database and then it will write the data which contains the name of the person which entered in the institute with the exact timing does the attendance will be mark for the person.

Figure 15: Capturing Video Function (Source: Self-Created in Pycharm)

The code in the figure above shows the initialization of cv2 every function that opens the webcam of the desktop or laptop or any system where this program has been embedded and records the faces of the person and analyzes it and matches it with the in-built database for registering the attendance. As the image which is captured is large in size and to save the database the image is then converted to the small size that is enough for the face recognition.

Figure 16: Finding Match (Source: Self-Created in Pycharm)

The above image shows the code for finding out the matches in the database for the input face images. The name is in the class names which is the actual name of the files which contains the person’s name with which the match is done and the result is stored in the indexes (Kuang et al. 2020).

The x-axis and the y axis has been initialized as the face location and put the access has been multiply by 4 for showing the rectangle at the face of a person and the color is set where the face is recognized the CV2 function is set for opening the webcam and analyze and the wait key as 1 is in slice to one and then the result is provided as image.

As it can be seen the attendance system is working properly and each face is detected and the time is recorded in the csv file which can be later used any software which support CSV for maintaining the attendance of the student (Gupta et al. 2020). The function in the program restricts the recording of the attendance if the student has once entered the institution and thus reducing the chance of the duplicity and the performance of the program that has been implemented as expected, as shown in the simple program.

Discussion

The section discussed the development of the program using the method that has been selected after reviewing the various papers that have been collected from the various sources and then after appropriate articles and books have been selected which provided an idea and strategy plan to how the program should be implemented.

Face detection was first invented by “viola and Michael Jones”. The system that was developed then was capable of detecting faces from the various cameras but in this project the system that will be used for free detection is called “histogram of oriented gradient or in sort of HOG”. The image is converted to black and white for detecting the face as the color data will not require the pixel in the image to be captured at a time and every pixel surrounding it will be evaluated.

The main goal of this process is to identify the dark pixel compared to the current fixing surrounding it the arrow is drawn the direction of the pixel where it is getting dark the process is again repeat it for each pixel in the image and replaced by the arrow the arrow which has called Gradients and they help in showing the flow of the light throughout the image (Garcia et al. 2021).

The direction of brightness can be calculated using the errors and this process solve the problem and make the task very easier for the representation (Srivastava et al. 2021). The greater and which has been generated using the arrows right much deeper details following the basic flow of light it is easy to determine the pattern of the image.

The first step is to find all faces in frame using the pipeline. The pattern is then broken into the sizes of 16×16 pixels for each image the square is then counted for the population of gradients in its point and the direction then the square is being replaced (Pawaskar et al. 2020).

The end output that we get after pressing all the rectangles at the place of a rose of the image originally used while the representations provide the basic structures of facial representation in a very basic way. The faces are there in the fight by HOG image and to find that the pattern which is generated by HOG are extracted from the model which has been trained to analyze the face.

Using the similar technique all the faces can be identified utilizing the patterns of hog. The presentation of this program can be easily done using python and Dilip to generate the hope pattern.

The next step is to project the face and isolate it from the images. During the process a problem is the different angles from where the image has been recorded and for solving this problem the image is walked and each image is checked for the position of eyes and lips and the makes it a lot easier for solving the face detection problem and comparing it with the database.

The algorithm which is used is known as face landmark estimation. This algorithm marks the 68 points specifically in the face by generating the facial structure of person for example the point is created on top of the chain and ages of each eye the eyebrows are also covered with this points and does the machine learning is used to train the model specifying the 68 points in any face the result can be produced.

Figure 17: Facial Points (Source: Internet)

As all the important points have been gathered using the algorithm now it’s possible to rotate and scale the image so that eye and mouth can be centered for the process. As the problem of detecting the person’s face from various angles can be worked and make it look like straight facing and can be compared with the database which is inbuilt in the system.

This final step is perhaps the simplest of the entire procedure. All we have to do now is look through our knowledge base for the individual who looks the most like our test image.

Any typical machine learning classification algorithm can be used for this. It is not necessary to use advanced deep learning techniques (Bairagi et al. 2021). It uses a straightforward linear “SVM classifier”, but any classification algorithm would suffice. All it has to do now is train a classifier to take measures from a fresh test image and determine which knowledge is the most similar.

This classification takes a few milliseconds to complete. The individual is the result’s classification name embedded in the generated or compared image.

Conclusion

The chapter described and explained the development and deployment of a real-time AI-based “students attendance system” that was created for taking the institution’s automatic attendance.

As the traditional method of collecting attendance necessitates a lot of effort and large amounts of paperwork that are difficult to arrange, and there is always the risk of making a mistake in the attendance register, which also adds to the cost.to avoid this most alternative method of taking attendance that ensures high security and accuracy while requiring no maintenance.

A quick study of the numerous findings made while developing the software, as well as a discussion of various issues and arguments for picking the method. The program has been developed and tested with the in database and with the webcam and results showed the highest accuracy even if the image that has been captured from the different views.

5.0 Conclusion.

5.1 Introduction.

This chapter helps in providing of the details of the overall works which have been done entirely into this research paper.

Which also consists of the useful findings into the analysis section however, this chapter will also help to understand about the findings which are being linked with the objectives in order to determine the overall effectiveness which are meant to meet the aims which have been stated into this research paper.

Moreover this chapter also consists of the recommendation part which clearly states about the future developments which can be done in order to enhance the effectiveness of the system.

5.2 Linking With Objectives.

The useful findings along with the analysis which have been obtained from the findings as well as objectives are being linked so as to validate the statements which have been portrayed into this entire research work in order to validate the research objectives which has been made into this research study.

To develop a smart and portable attendance system which is easy to use and is self-powered.

The empirical study section consists of reviews of the published papers on which the researchers have been researched. Which critically stated about the use of RFID devices which majorly uses QR codes to detect the approvals (Srivastava et al. 2021).

That helps in making of the attendance in an efficient and effective way which will also ensure in giving the arrival as well as departure times as it is being connected through the internet and the data are being procured into the system for future use.

To ensure speedy recording of attendances for less than three seconds.

The use of a support vector machine into a facial recognition system helps in acquiring of the attendances by determining the face of the individuals within a fraction of time i.e. 3 seconds from the data which are being stored into the database (Shbib et al. 2019). This also helps in reducing the failures and the accuracy which is being obtained from this system is 99.6%.

To have a huge memory space to store the data effectively.

This system of recognition of the face of the human beings in attaining attendance into an educational institutions or into an working organization is being completely operated by the help of high speed internet connectivity which helps in procurement of the data into the database and which also allows to save the attendance activity log report to the cloud storage which is being synced from time to time. That helps in maintaining the records.

To create a user friendly database in managing attendances.

This system requires a human being if it fails at some time or does not function properly due to the physical damages which are being attained to the system (Shaik and Islam 2019). In order to understand the problem which has been faced by this system, it allows the persons to deliver the required information in the places where the problem has actually happened and which hampered the whole system.

Moreover this system also allows the members of the organization to check their daily or monthly attendances which are being implemented into this system as it is completely based on a web browser.

To investigate the proper identification which shows the entire system is successful or not.

This system allows to investigate on the individual’s activity log sheets if the attendance has not been taken by the web cameras. This system sends a follow up messages in the form of SMS and email to the respective persons which will help to make sure the individual that the attendance has been noted down or which have not been noted down (Yasmin et al. 2020).

This sending of follow up messages to the individual will help to make sure that the attendance has been noted down or which have not.

If the attendance is not noted down then the person will have to contact with the concerned authority and demonstrate about the problems in order to make a speedy recovery to the problems. Moreover, this system will again take the necessary data to train and make an effective result in determining the person.

5.3 Recommendations.

The facial recognition system has been considered as one of the significant discoveries which helps in noting down of the attendances of the peoples whose data’s are being stored into the system.

Apart from noting down of the attendances into this respective databases which helps in maintaining the activity log that is being presented in the form of excel sheets in the online mode, it also helps in detecting the behavioral changes of the individuals by the help of huge algorithms as well as programming languages (Satpute et al. 2019).

Thus this system also helps in locating the persons into specific organizations and also provides the time graphs in which the places that has been mostly visited by the person are being showcased.

Thus the useful recommendations which will be discussed in this entire section will majorly focus on the overall costs that are being considered as one of the huge burdens faced by the small organizations as well as institutions in implementing this process.

In fact this system should consist of the customization part on which the entire system can be developed as per the needs of the organization which will help to lower the costs.

The most common problem which is being faced in this system is from illumination and aging. It is being recommended to use the night vision webcams which are capable to detect the face in low lighting conditions effectively in order to reduce the disruptions which are being faced by the low illumination (Chowdhury et al. 2020).

On the other hand the system should also train itself from the daily capturing of the images which will help to modify the search results from the given database of images and which will also restrict the system to completely eliminate the problem which is being faced from aging.

5.4 Conclusion.

This is the summarization part of the entire chapter i.e. conclusion. In which the objectives which are being linked with the analysis as well as to that of the findings has been discussed which also helps in validating the entire statements which have been portrayed in the entire section of the research paper to make a complete understanding. Moreover, this chapter consisted of the useful recommendation part which also acts as a weightage in this entire research paper.

As it suggests the most useful ways in which the system can be improvised so as to derive the results in an efficient way. Which will also help to improve the performance of the system in the near future in order to completely eliminate any sort of problems for procuring attendance.

Furthermore, this research paper also helps in highlighting the various aspects which are being leaded for the betterment of the entire process and which will help the organizations as well as the educational institutions in procuring the attendances which will help to save huge time as well as efforts.

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