Face Recognition Based Smart Attendance System

Face Recognition Based Smart Attendance System | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Face Recognition Based Smart Attendance System Prof. Javed Patel, Mr. Prateek Rasalkar This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4396816/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Face Recognition based attendance is a modern- day technique to take attendance. It can be used in various places such as schools, offices, parliaments, etc. The main aim of this system is to enhance and upgrade the present attendance system which would be biometric r signature based. The current system has a lot of faults and inaccurate in taking attendance and has various disadvantages. This is the reason behind face recognition- based attendance system. We are using the natural traits of human body that is human face. As each has unique and different identity therefore the possibility to deviate from the result is very low Artificial Intelligence and Machine Learning Face Recognition Smart Attendance System Open CV Numpy Figures Figure 1 Figure 2 Figure 3 Figure 4 I. INTRODUCTION This is the generation of the Internet, and technology has completely changed humans. One such application of the technology is a face recognition-based smart attendance system. The most important thing is attendance, whether it be in a classroom or an office. The more efficient and accurate the attendance system, the better the performance of the student or employee. In the past, the teacher would call your roll number, and you would sign against your name. These methods were not accurate, as any would be a proxy and also time-consuming. After a few years, a biometric attendance system came into existence, which was mainly used in offices. It was very costly and would be sufficient for a limited number of people as compared to school. This biometric attendance system was unable to be used during the COVID-19 pandemic as we needed to submit our biometrics, which again needed to be cleaned or sanitized. Thus, we introduced a face recognition-based attendance system that is both accurate and efficient. Face recognition is a child of image processing. Image processing is basically the process of extracting data from a digital image. We would be receiving the digital image and then extracting the data using algorithms and programs. Face recognition is achieved using an algorithm called the Haar-Cascade, which is simpler as compared to the Vivian-Jones system, which will also combine with facial recognition algorithms like the Eigen faces and original double pattern histogram [ 1 ]. Another important thing is that the attendance sheet, or Excel sheet, is accessible to the third party. The third party here refers to parents, as they even get to know their children's attendance in class. II. OBJECTIVE OF THE PROJECT In order to solve the drawbacks of the previous attendance system, the system must be changed. The proposed system will be more accurate, time-efficient, and have zero paper work. The following are the objectives of the project: - To develop an attendance system, this is handy and self-powered. To ensure the speed of the attendance system is higher than the previous system. Have sufficient storage memory in order to store attendance of a school or office. Allow teacher, boss or parents with the attendance sheet. Allow teacher easily to add new student with the help of GUI (Graphical User Interface). Allows a user-friendly interface for admins to access the system. Able to display to the user a message indicating the success or failure of the face-recognition process. The database of this attendance system can hold up to 2000 individual’s information. III. LITERATURE REVIEW Traditional method of marking attendance in school / colleges manually was very time-consuming method which causes delay to start the lecture. This method of taking attendance of students on long register books is now no longer applicable in many schools and colleges. Teacher manually calling for each student’s name and marking the attendance or passing the attendance sheets to every student so that each student will mark his/her attendance that too in a fair manner was a poor method which mostly aims to disturbing the whole class. Apart from traditional attendance system’s various solutions has emerge such as taking attendance using RFID, Fingerprint/Thumbprint, Smartcards, smartphones and face recognition. RFID based attendance system would require direct contact of the person to update their attendance. However, for large classes it Is not efficient as students need to wait in the long queue and wait until their turn comes to mark the attendance which leads to delay. Another method is by using mobile applications to update the attendance of the student by enabling the geolocation feature. This will track every moment of the student in the campus. But for some cases problem may arise such as Mobile phone may run out of the battery or Network connection gets disconnected which causes problem for the attendance system to track and mark the attendance. Lastly, face recognition-based attendance system, the system camera captures the photo of the student and that captured image is compared with the face database to search for the students face and marks the attendance after successfully matching. This face recognition system requires no direct contact or interaction with the system leading to much efficient method. Depending upon more advanced technology, the accuracy of the face recognition algorithm can be more improved and apply it for better Attendance Management System [ 3 ]. A) Proposed System Architecture The proposed system automates the attendance system of educational Institutions and reduces the shortcoming of the existing manual system. A smart device that can recognize each person's identification and eventually save attendance information in a database system will be created. All the scholars of the class must register themselves by entering the needed details and also their images will be captured and stored in the dataset. The aimed of this design is to mark the attendance using automatic face recognition technology. The system records videotape feeds from the camera and detects the faces in image format [ 1 ]. The detected faces are compared with the law database and marked attendance within the Excel train. Using these Excel sheets, we will generate a graph that shows the attendance of the entire class/Individual student. The function of the proposed system is to capture the face of every student and keep it for them to attend. The face of the expert must be taken in such a way that everyone can see what the student's face is like. There's no need for the school teacher to be physically present in the classroom because the system records the videotape and also with nonstop face processing way is covered monitored and therefore the attendance is updated. All Individuals in the category must register themselves by entering the information they require so that their photos can be taken and stored within the database. During each session, a face is found in the live streaming video of the class. The recovered faces are compared with the being images in the database. Once a match is found, attendees are marked on the appropriate reader. At the top of each session, a list of present students will be sent to the appropriate faculty in charge of the session. B) Architecture The system architecture of the proposed system is given above. IV. METHODOLGY 1. Image Capture : The student’s images are captured who are presentin the class. 2. Face Detection : The performance of face recognition systems is always improved by a good and effective face detection algorithm. 3. Pre-Processing : Pre-processing is applied after the detected face is uprooted. This step involves with histogram equalization of the uprooted face image. 4. Database Development : Images of each student is collected as training images for this project. 5. Feature Extraction and Classification : Feature Extraction and Classification: In order to obtain correct results, the performance of aFace Recognition system is also dependent on feature extraction and classification. Either point- grounded or comprehensive ways can be used for point birth. 6. Post-Processing : After identifying a student's face in the proposed system, the date, in time, and out time of that student are displayed or updated in the database. A) Algorithm For Proposed System a) Haar Cascade Algorithm: It is an object detection system that can recognize faces in still photos. In order to fete a face, Haar waterfall classifiers are trained on a large number of filmland, both with and without faces. This means that instead of choosing non- facial areas, Haar classifiers scan the window for faces. According to research, image processing and pattern recognition experts have found it difficult to reliably identify human faces. An object discovery system called the Haar Cascade Algorithm may be used to find faces, climbers, objects, and facial feelings. Haar Cascade Classifier [ 2 ] is one of the best detectors for face detection from a picture in terms of speed and accuracy. The most important component of the face detection Haar Cascade Classifier is the Haar characteristics. While the Haar Cascade Classifier features can descry any item, these features are employed to determine if a point is present in a procedures were employed in the disquisition [ 5 ]. A waterfall function is developed using the colorful amounts of filmland, both positive and negative, in this machine literacy- grounded fashion. It is then utilized to find things in other images in that situation. To increase classification performance in this study, the system requires a large number of positive pictures (photos with faces) and negative images (photos without faces). b) Face Recognition Working: - Based on the image above, let’s break it into several small steps so we can understand iteasily: Let's say that we have a grey scale picture of our faces. We can get portion of this picture as a window of 3x3 pixels. It can too be spoken to as a 3x3 lattice containing the escalated of each pixel (0 ~ 255). This esteem will be utilized to characterize the modern values from the 8 neighbours. For each neighbour of the central esteem (limit), we set a unused parallel esteem. We set 1 for values rise to or higher than the limit and 0 for values lower than the threshold. Now, the framework will contain as it were twofold values (disregarding the central esteem). We require concatenating each double esteem from each position from the framework line by line into a modern twofold esteem (e.g. 10001101). Note: a few creators utilize other approaches to concatenate the parallel values (e.g. clockwise heading), but the last result will be the same. Then, we change over this double esteem to a decimal esteem and set it to the central esteem of the network, which is really a pixel from the unique image. At the conclusion of this method (LBP method), we have a unused picture which speaks to superior the characteristics. V. IMPLEMEMTATION On fruitful execution of the program this GUI will show up. Here you will see the different icons/buttons with its working title underneath image. Clicking the specific button will open another GUI window performing specialized task. As we can see, here add up to of 2 buttons are utilized. You can include more if you need. This interface is completely made utilizing Tkinter library in python. On clicking the understudy points of interest button on opening interface this sort of window will show up. Here different section areas and combo boxes can be seen. The points of interest require to be put away are written here. There two radio buttons utilized for photographs alternative climate you wish to take a photo or not. There are a few working buttons at the cleared-out foot of the window [6]. The take button will dispatch a camera. This can be a web camera or any outside coordinates camera. The camera on opening will consequently take 50 test photographs of a same individual. These captured photographs will be put away in envelope in the shape of .jpg arrange. This all will be the changed over gray scale pictures of the captured test photos. The current screen is freezed on 9th test whereas capturing add up to of 50 pictures. As we can see, the put away subtle elements can be seen on right side of window. There’s too a scroll bar highlight present. VI. CONCLUSION This design introduced the effective and accurate system of attendance in the classroom terrain that can replace the old homemade system. This system is secure enough, dependable and available for installing the system in the classroom. It can be constructed using a camera and computer. There's a need to use some ways that can fete the faces in robe to ameliorate the system performance. As a result, we were suitable to produce a robust and productive participation structure in order to apply an image handling algorithm for relating faces in the classroom. In future, the system can be made more effective so that it can fete the faces in all lighting conditions and with further delicacy. The algorithm can be bettered to fete scholars through eyes so that identical halves can be discerned. VII. DECLARATIONS Funding - No funding was received to assist with the preparation of this manuscript. Competing Interests- The authors have no competing interests to declare that are relevant to the content of this article. Ethics approval - Not Applicable Consent to participate – Not Applicable. Consent for publication –Not Applicable. Availability of data-The data are is a github repository. https://github.com/prateekr0000/Face- Recognition-Based-Smart-Attendance-System.git contains the data. Code Availability- The data are in a github repository. https://github.com/prateekr0000/Face- Recognition-Based-Smart-Attendance-System.git contains the code. Authors' contributions – Study conception and design: Prateek Rasalkar, Prof Javed Patel; Data collection: Prateek Rasalkar:- analysis and interpretation of results Draft manuscript preparation: Prateek Rasalkar:-All authors reviewed the results and approved the final version of the manuscript. References P. Mehta, “An Efficient Attendance Management System based on Face Recognition using Matlab and Raspberry Pi 2,” International Journal of Engineering Technology Science and Research IJETSR , 2016; 3(5): 71–78. H. P. LeBlanc, “The Relationship between Attendance and Grades in the College Classroom,” 17th Annual Meeting of the International Academy of Business Disciplines, Pittsburg Pennsylvania , 2005; 643(210):1–19. A. A. Mohammed and U. Jyothi Kameswari, “ Web- Server based Student Attendance System using RFID Technology , ” International Journal of Engineering Trends and Technology (IJETT), 2013; 4(5):1559–1563. S. C. Gaddam and N. V. K. Ramesh, “Attendance management and user security system’s based on Eigen faces algorithm using Raspberry pi 2 and Ethernet,” Indian Journal of Science and Technology , 2016; 9(17): 8107–8112. P. S. S. Srivignessh and M. Bhaskar, “RFID and pose invariant face verification based automated classroom attendance system , ” Inter-national Conference on Microelectronics, Computing and Communication, MicroCom, 2016:1-6. V. O. Adeniji, M. S. Scott, and N. Phumzile, “Development of an Online Biometric- enabled Class Attendance Register System,” IST-Africa 2016 Conference Proceedings Paul Cunningham and Miriam Cunningham (Eds), 2016: 1–8. J. Lámer, O. Kainz, and F. Jakab, “Marker based attendance systems in education process,” 13th International Conference on Emerging eLearning Technologies and Applications (ICETA) , 2015. S. Maravi, R. Pinter, V. Vojni, V. Tumbas,, “Smartphone Application for Tracking Students’ Class Attendance,” SISY 2016 IEEE 14th International Symposium on Intelligent Systems and Informatics August, 2016: 227–232. J. D. Sweetlin, V. Aswini, and R. Dhanusha, “Speech Based Attendance Application Register,” Fifth International Conference On Recent Trends In Information Technology , 2016:1-5. P. Wagh, “Attendance System based on Face Recognition using Eigen face and peA Algorithms,” International Conference on Green Computing and Internet of Things (ICGCIoT), 2015: 303–308. Additional Declarations The authors declare no competing interests. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4396816","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":300753103,"identity":"7930b189-fa1a-4ec1-8492-c72500f64117","order_by":0,"name":"Prof. Javed Patel","email":"","orcid":"","institution":"Vidyalankar Institute of Technology Mumbai","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Prof.","middleName":"Javed","lastName":"Patel","suffix":""},{"id":300753104,"identity":"1bbe6307-5cdd-4a54-a71b-dacc0abcf7b9","order_by":1,"name":"Mr. Prateek Rasalkar","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA+ElEQVRIiWNgGAWjYNCCggQGBvbGxgcfgGw2dqK0GAC18BxuNpwB0sJMtBaJ9DZhHhCHkBZz6cOPP/wwSJM3n5HYxmzza5s8HzMD44ePObi1WPalmUn2GOQYzjnzsO1xbt9twzZmBmbJmdvwOOkMgxkDj0EF4wz2xHbj3J7bjEAtbMy8eLWwf/74x6DCfgZDYpu0Zc9teyK08BhI8xjkJM7gAGph+HE7kaAWyx6eMmkZg7TkGTwHmw17G24ntzEzNuP1izkP++aPbyqSbWewtz988OPPbdv57c0HP3zE5zAUHmMbmGzArR5DC8MfvIpHwSgYBaNghAIAOoxP4KHLMc4AAAAASUVORK5CYII=","orcid":"https://orcid.org/0009-0008-6890-1792","institution":"Vidyalankar Institute of Technology Mumbai","correspondingAuthor":true,"submittingAuthor":false,"prefix":"Mr.","firstName":"Prateek","middleName":"","lastName":"Rasalkar","suffix":""}],"badges":[],"createdAt":"2024-05-09 18:43:36","currentVersionCode":1,"declarations":{"humanSubjects":true,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":true,"humanSubjectConsent":true,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false,"coiExplicitlySet":false},"doi":"10.21203/rs.3.rs-4396816/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4396816/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":56354660,"identity":"1e545b34-324f-44e3-986e-756bdd7cd0f3","added_by":"auto","created_at":"2024-05-13 05:50:17","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":63558,"visible":true,"origin":"","legend":"\u003cp\u003eUnnumbered Image in the Methodolgy Section.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-4396816/v1/7143b611b085245b42d1343e.png"},{"id":56354658,"identity":"7d5e405e-2d92-41f2-b25c-0f8a6b074383","added_by":"auto","created_at":"2024-05-13 05:50:17","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":47038,"visible":true,"origin":"","legend":"\u003cp\u003eUnnumbered Image in the Architecture Section.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-4396816/v1/82c1b2430aa24c83d689b883.png"},{"id":56354657,"identity":"e71bda18-c20c-4876-9175-0871a7e00a3c","added_by":"auto","created_at":"2024-05-13 05:50:17","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":54915,"visible":true,"origin":"","legend":"\u003cp\u003eUnnumbered Image in the Face Recognition Working Section.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-4396816/v1/13b2d87264f314c9078cea08.png"},{"id":56354659,"identity":"5733461a-203d-4b8e-81c8-ae4b85137e69","added_by":"auto","created_at":"2024-05-13 05:50:17","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":185644,"visible":true,"origin":"","legend":"\u003cp\u003eUnnumbered Image in the Results Section.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-4396816/v1/93df089d90f4b3edfddeae38.png"},{"id":56354662,"identity":"df9a4671-edc8-4b67-a846-c31da546b041","added_by":"auto","created_at":"2024-05-13 05:50:21","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":608746,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4396816/v1/0cc148d4-a7c7-450d-a5a5-212c911c55c4.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003eFace Recognition Based Smart Attendance System\u003c/p\u003e","fulltext":[{"header":"I.\tINTRODUCTION","content":"\u003cp\u003eThis is the generation of the Internet, and technology has completely changed humans. One such application of the technology is a face recognition-based smart attendance system. The most important thing is attendance, whether it be in a classroom or an office. The more efficient and accurate the attendance system, the better the performance of the student or employee. In the past, the teacher would call your roll number, and you would sign against your name. These methods were not accurate, as any would be a proxy and also time-consuming. After a few years, a biometric attendance system came into existence, which was mainly used in offices. It was very costly and would be sufficient for a limited number of people as compared to school. This biometric attendance system was unable to be used during the COVID-19 pandemic as we needed to submit our biometrics, which again needed to be cleaned or sanitized.\u003c/p\u003e \u003cp\u003eThus, we introduced a face recognition-based attendance system that is both accurate and efficient. Face recognition is a child of image processing. Image processing is basically the process of extracting data from a digital image. We would be receiving the digital image and then extracting the data using algorithms and programs. Face recognition is achieved using an algorithm called the Haar-Cascade, which is simpler as compared to the Vivian-Jones system, which will also combine with facial recognition algorithms like the Eigen faces and original double pattern histogram [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Another important thing is that the attendance sheet, or Excel sheet, is accessible to the third party. The third party here refers to parents, as they even get to know their children's attendance in class.\u003c/p\u003e"},{"header":"II.\tOBJECTIVE OF THE PROJECT","content":"\u003cp\u003eIn order to solve the drawbacks of the previous attendance system, the system must be changed. The proposed system will be more accurate, time-efficient, and have zero paper work.\u003c/p\u003e\n\u003cp\u003eThe\u0026nbsp;following\u0026nbsp;are\u0026nbsp;the\u0026nbsp;objectives\u0026nbsp;of\u0026nbsp;the\u0026nbsp;project:\u0026nbsp;-\u003c/p\u003e\n\u003col\u003e\n \u003cli\u003eTo develop an attendance system, this is handy and self-powered.\u003c/li\u003e\n \u003cli\u003eTo\u0026nbsp;ensure\u0026nbsp;the\u0026nbsp;speed\u0026nbsp;of\u0026nbsp;the\u0026nbsp;attendance\u0026nbsp;system\u0026nbsp;is higher than the previous system.\u003c/li\u003e\n \u003cli\u003eHave\u0026nbsp;sufficient\u0026nbsp;storage\u0026nbsp;memory\u0026nbsp;in\u0026nbsp;order\u0026nbsp;to\u0026nbsp;store attendance of a school or office.\u003c/li\u003e\n \u003cli\u003eAllow\u0026nbsp;teacher,\u0026nbsp;boss\u0026nbsp;or\u0026nbsp;parents\u0026nbsp;with\u0026nbsp;the\u0026nbsp;attendance sheet.\u003c/li\u003e\n \u003cli\u003eAllow\u0026nbsp;teacher\u0026nbsp;easily\u0026nbsp;to\u0026nbsp;add\u0026nbsp;new\u0026nbsp;student\u0026nbsp;with\u0026nbsp;the help of GUI (Graphical User Interface).\u003c/li\u003e\n \u003cli\u003eAllows\u0026nbsp;a\u0026nbsp;user-friendly\u0026nbsp;interface\u0026nbsp;for\u0026nbsp;admins\u0026nbsp;to\u0026nbsp;access the system.\u003c/li\u003e\n \u003cli\u003eAble to display to the user a message indicating the success or failure of the face-recognition process.\u003c/li\u003e\n \u003cli\u003eThe database of this attendance system can hold up to 2000 individual’s information.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"III. LITERATURE REVIEW","content":"\u003cp\u003eTraditional method of marking attendance in school / colleges manually was very time-consuming method which causes delay to start the lecture. This method of taking attendance of students on long register books is now no longer applicable in many schools and colleges. Teacher manually calling for each student\u0026rsquo;s name and marking the attendance or passing the attendance sheets to every student so that each student will mark his/her attendance that too in a fair manner was a poor method which mostly aims to disturbing the whole class.\u003c/p\u003e\n\u003cp\u003eApart from traditional attendance system\u0026rsquo;s various solutions has emerge such as taking attendance using RFID, Fingerprint/Thumbprint, Smartcards, smartphones and face recognition. RFID based attendance system would require direct contact of the person to update their attendance. However, for large classes it Is not efficient as students need to wait in the long queue and wait until their turn comes to mark the attendance which leads to delay. Another method is by using mobile applications to update the attendance of the student by enabling the geolocation feature. This will track every moment of the student in the campus. But for some cases problem may arise such as Mobile phone may run out of the battery or Network connection gets disconnected which causes problem for the attendance system to track and mark the attendance.\u003c/p\u003e\n\u003cp\u003eLastly, face recognition-based attendance system, the system camera captures the photo of the student and that captured image is compared with the face database to search for the students face and marks the attendance after successfully matching. This face recognition system requires no direct contact or interaction with the system leading to much efficient method. Depending upon more advanced technology, the accuracy of the face recognition algorithm can be more improved and apply it for better Attendance Management System [\u003cspan class=\"CitationRef\"\u003e3\u003c/span\u003e].\u003c/p\u003e\n\u003cp\u003e\u003cspan\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e\u003cspan type=\"BoldSmallCaps\" class=\"BoldSmallCaps\" name=\"Emphasis\"\u003eA) Proposed System Architecture\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e\u003c/p\u003e\n\u003cp\u003eThe proposed system automates the attendance system of educational Institutions and reduces the shortcoming of the existing manual system. A smart device that can recognize each person\u0026apos;s identification and eventually save attendance information in a database system will be created. All the scholars of the class must register themselves by entering the needed details and also their images will be captured and stored in the dataset.\u003c/p\u003e\n\u003cp\u003eThe aimed of this design is to mark the attendance using automatic face recognition technology. The system records videotape feeds from the camera and detects the faces in image format [\u003cspan class=\"CitationRef\"\u003e1\u003c/span\u003e]. The detected faces are compared with the law database and marked attendance within the Excel train. Using these Excel sheets, we will generate a graph that shows the attendance of the entire class/Individual student. The function of the proposed system is to capture the face of every student and keep it for them to attend.\u003c/p\u003e\n\u003cp\u003eThe face of the expert must be taken in such a way that everyone can see what the student\u0026apos;s face is like. There\u0026apos;s no need for the school teacher to be physically present in the classroom because the system records the videotape and also with nonstop face processing way is covered monitored and therefore the attendance is updated. All Individuals in the category must register themselves by entering the information they require so that their photos can be taken and stored within the database. During each session, a face is found in the live streaming video of the class. The recovered faces are compared with the being images in the database. Once a match is found, attendees are marked on the appropriate reader. At the top of each session, a list of present students will be sent to the appropriate faculty in charge of the session.\u003c/p\u003e\n\u003cp\u003e\u003cspan\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e\u003cspan type=\"BoldSmallCaps\" class=\"BoldSmallCaps\" name=\"Emphasis\"\u003eB) Architecture\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e\u003c/p\u003e\n\u003cp\u003eThe system architecture of the proposed system is given above.\u003c/p\u003e"},{"header":"IV. METHODOLGY","content":"\u003cp\u003e\u003cspan\u003e\u003cstrong\u003e1. Image Capture\u003c/strong\u003e: The student\u0026rsquo;s images are captured who are presentin the class.\u003cbr\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e\u003cspan\u003e\u003cstrong\u003e2. Face Detection\u003c/strong\u003e: The performance of face recognition systems is always improved by a good and effective face detection algorithm.\u003cbr\u003e\u003c/span\u003e\u003cspan\u003e\u003cstrong\u003e3. Pre-Processing\u003c/strong\u003e: Pre-processing is applied after the detected face is uprooted. This step involves with histogram equalization of the uprooted face image.\u003cbr\u003e\u003c/span\u003e\u003cspan\u003e\u003cstrong\u003e4. Database Development\u003c/strong\u003e: Images of each student is collected as training images for this project.\u003cbr\u003e\u003c/span\u003e\u003cspan\u003e\u003cstrong\u003e5. Feature Extraction and Classification\u003c/strong\u003e: Feature Extraction and Classification: In order to obtain correct results, the performance of aFace Recognition system is also dependent on feature extraction and classification. Either point- grounded or comprehensive ways can be used for point birth.\u003cbr\u003e\u003c/span\u003e\u003cspan\u003e\u003cstrong\u003e6. Post-Processing\u003c/strong\u003e: After identifying a student\u0026apos;s face in the proposed system, the date, in time, and out time of that student are displayed or updated in the database.\u003cbr\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e\u003cspan type=\"BoldSmallCaps\" class=\"BoldSmallCaps\" name=\"Emphasis\"\u003e\u003cstrong\u003eA) Algorithm For Proposed System\u003c/strong\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003ea) Haar Cascade Algorithm:\u003c/p\u003e\n\u003cp\u003eIt is an object detection system that can recognize faces in still photos. In order to fete a face, Haar waterfall classifiers are trained on a large number of filmland, both with and without faces. This means that instead of choosing non- facial areas, Haar classifiers scan the window for faces. According to research, image processing and pattern recognition experts have found it difficult to reliably identify human faces. An object discovery system called the Haar Cascade Algorithm may be used to find faces, climbers, objects, and facial feelings. Haar Cascade Classifier [\u003cspan class=\"CitationRef\"\u003e2\u003c/span\u003e] is one of the best detectors for face detection from a picture in terms of speed and accuracy.\u003c/p\u003e\n\u003cp\u003eThe most important component of the face detection Haar Cascade Classifier is the Haar characteristics. While the Haar Cascade Classifier features can descry any item, these features are employed to determine if a point is present in a\u003c/p\u003e\n\u003cp\u003eprocedures were employed in the disquisition [\u003cspan class=\"CitationRef\"\u003e5\u003c/span\u003e]. A waterfall function is developed using the colorful amounts of filmland, both positive and negative, in this machine literacy- grounded fashion. It is then utilized to find things in other images in that situation. To increase classification performance in this study, the system requires a large number of positive pictures (photos with faces) and negative images (photos without faces).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eb) Face Recognition Working: -\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBased on the image above, let\u0026rsquo;s break it into several small steps so we can understand iteasily:\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003e\n \u003cp\u003eLet\u0026apos;s say that we have a grey scale picture of our faces.\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003eWe can get portion of this picture as a window of 3x3 pixels.\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003eIt can too be spoken to as a 3x3 lattice containing the escalated of each pixel (0\u0026thinsp;~\u0026thinsp;255).\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003eThis esteem will be utilized to characterize the modern values from the 8 neighbours.\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003eFor each neighbour of the central esteem (limit), we set a unused parallel esteem. We set 1 for values rise to or higher than the limit and 0 for values lower than the threshold.\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003eNow, the framework will contain as it were twofold values (disregarding the central esteem). We require concatenating each double esteem from each position from the framework line by line into a modern twofold esteem (e.g. 10001101). Note: a few creators utilize other approaches to concatenate the parallel values (e.g. clockwise heading), but the last result will be the same.\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003eThen, we change over this double esteem to a decimal esteem and set it to the central esteem of the network, which is really a pixel from the unique image.\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003eAt the conclusion of this method (LBP method), we have a unused picture which speaks to superior the characteristics.\u003c/p\u003e\n \u003c/li\u003e\n\u003c/ul\u003e"},{"header":"V.\tIMPLEMEMTATION","content":"\u003cp\u003eOn fruitful execution of the program this GUI will show up. Here you will see the different icons/buttons with its working title underneath image. Clicking the specific button will open another GUI window performing specialized task. As we can see, here add up to of 2 buttons are utilized. You can include more if you need. This interface is completely made utilizing Tkinter library in python.\u003c/p\u003e\n\u003cp\u003eOn clicking the understudy points of interest button on opening interface this sort of window will show up. Here different section areas and combo boxes can be seen. The points of interest require to be put away are written here. There two radio buttons utilized for photographs alternative climate you wish to take a photo or not. There are a few working buttons at the cleared-out foot of the window \u003cstrong\u003e[6].\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe take button will dispatch a camera. This can be a web camera or any outside coordinates camera.\u003c/p\u003e\n\u003cp\u003eThe camera on opening will consequently take 50 test photographs of a same individual. These captured photographs will be put away in envelope in the shape of .jpg arrange. This all will be the changed over gray scale pictures of the captured test photos. The current screen is freezed on 9th test whereas capturing add up to of 50 pictures. As we can see, the put away subtle elements can be seen on right side of window. There\u0026rsquo;s too a scroll bar highlight present.\u003c/p\u003e"},{"header":"VI. CONCLUSION","content":"\u003cp\u003eThis design introduced the effective and accurate system of attendance in the classroom terrain that can replace the old homemade system. This system is secure enough, dependable and available for installing the system in the classroom. It can be constructed using a camera and computer. There's a need to use some ways that can fete the faces in robe to ameliorate the system performance.\u003c/p\u003e\n\u003cp\u003eAs a result, we were suitable to produce a robust and productive participation structure in order to apply an image handling algorithm for relating faces in the classroom. In future, the system can be made more effective so that it can fete the faces in all lighting conditions and with further delicacy. The algorithm can be bettered to fete scholars through eyes so that identical halves can be discerned.\u003c/p\u003e"},{"header":"VII. DECLARATIONS","content":"\u003cul\u003e\n \u003cli\u003eFunding - No funding was received to assist with the preparation of this manuscript.\u003c/li\u003e\n \u003cli\u003eCompeting Interests- The authors have no competing interests to declare that are relevant to the content of this article.\u003c/li\u003e\n \u003cli\u003eEthics\u0026nbsp;approval\u0026nbsp;-\u0026nbsp;Not\u0026nbsp;Applicable\u003c/li\u003e\n \u003cli\u003eConsent\u0026nbsp;to\u0026nbsp;participate\u0026nbsp;–\u0026nbsp;Not\u0026nbsp;Applicable.\u003c/li\u003e\n \u003cli\u003eConsent\u0026nbsp;for\u0026nbsp;publication\u0026nbsp;–Not\u0026nbsp;Applicable.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cul\u003e\n \u003cli\u003eAvailability\u0026nbsp;of\u0026nbsp;data-The\u0026nbsp;data\u0026nbsp;are\u0026nbsp;is\u0026nbsp;a\u0026nbsp;github repository.\u003cu\u003ehttps://github.com/prateekr0000/Face-\u003c/u\u003e \u003cu\u003eRecognition-Based-Smart-Attendance-System.git\u003c/u\u003e contains\u0026nbsp;the data.\u003c/li\u003e\n \u003cli\u003eCode\u0026nbsp;Availability-\u0026nbsp;The\u0026nbsp;data\u0026nbsp;are\u0026nbsp;in\u0026nbsp;a\u0026nbsp;github\u0026nbsp;repository.\u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003cu\u003ehttps://github.com/prateekr0000/Face-\u003c/u\u003e \u003cu\u003eRecognition-Based-Smart-Attendance-System.git\u003c/u\u003e contains the code.\u003c/li\u003e\n \u003cli\u003eAuthors'\u0026nbsp;contributions\u0026nbsp;–\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eStudy\u0026nbsp;conception\u0026nbsp;and\u0026nbsp;design:\u0026nbsp;Prateek\u0026nbsp;Rasalkar, Prof Javed Patel;\u003c/p\u003e\n\u003cp\u003eData collection: Prateek Rasalkar:- analysis and interpretation of results\u003c/p\u003e\n\u003cp\u003eDraft manuscript preparation: Prateek Rasalkar:-All authors reviewed the results and approved the final version of the manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eP. Mehta, \u0026ldquo;An Efficient Attendance Management System based on Face Recognition using Matlab and Raspberry Pi 2,\u0026rdquo; \u003cem\u003eInternational Journal of Engineering Technology\u0026nbsp;Science\u0026nbsp;and\u0026nbsp;Research\u0026nbsp;IJETSR\u003c/em\u003e, 2016; 3(5): 71\u0026ndash;78.\u003c/li\u003e\n \u003cli\u003eH. P. LeBlanc, \u0026ldquo;The Relationship between Attendance and Grades in the College Classroom,\u0026rdquo; \u003cem\u003e17th\u0026nbsp;Annual\u0026nbsp;Meeting of the International Academy of Business Disciplines, Pittsburg Pennsylvania\u003c/em\u003e, 2005; 643(210):1\u0026ndash;19.\u003c/li\u003e\n \u003cli\u003eA. A. Mohammed and U. Jyothi Kameswari, \u003cem\u003e\u0026ldquo;\u003c/em\u003eWeb- Server based Student Attendance System using RFID Technology\u003cem\u003e,\u003c/em\u003e\u0026rdquo; \u003cem\u003eInternational Journal of Engineering Trends and Technology (IJETT),\u0026nbsp;\u003c/em\u003e2013; 4(5):1559\u0026ndash;1563.\u003c/li\u003e\n \u003cli\u003eS. C. Gaddam and N. V. K. Ramesh, \u0026ldquo;Attendance management and user security system\u0026rsquo;s based on\u0026nbsp;Eigen faces algorithm\u0026nbsp;using\u0026nbsp;Raspberry\u0026nbsp;pi 2\u0026nbsp;and\u0026nbsp;Ethernet,\u0026rdquo; \u003cem\u003eIndian\u0026nbsp;Journal of Science and Technology\u003c/em\u003e, 2016; 9(17): 8107\u0026ndash;8112.\u003c/li\u003e\n \u003cli\u003eP. S. S. Srivignessh and M. Bhaskar, \u0026ldquo;RFID and pose invariant face verification based automated classroom attendance system\u003cem\u003e,\u003c/em\u003e\u0026rdquo; \u003cem\u003eInter-national Conference on Microelectronics, Computing and Communication, MicroCom,\u0026nbsp;\u003c/em\u003e2016:1-6.\u003c/li\u003e\n \u003cli\u003eV. O. Adeniji, M. S. Scott, and N. Phumzile, \u0026ldquo;Development of an Online Biometric- enabled Class Attendance Register System,\u0026rdquo; \u003cem\u003eIST-Africa 2016 Conference Proceedings Paul Cunningham and Miriam Cunningham (Eds), 2016:\u0026nbsp;\u003c/em\u003e1\u0026ndash;8.\u003c/li\u003e\n \u003cli\u003eJ. L\u0026aacute;mer, O. Kainz, and F. Jakab, \u0026ldquo;Marker based attendance systems in education process,\u0026rdquo; \u003cem\u003e13th International Conference on Emerging eLearning Technologies and Applications (ICETA)\u003c/em\u003e, 2015.\u003c/li\u003e\n \u003cli\u003eS.\u0026nbsp;Maravi,\u0026nbsp;R.\u0026nbsp;Pinter,\u0026nbsp;V.\u0026nbsp;Vojni,\u0026nbsp;V.\u0026nbsp;Tumbas,,\u0026nbsp;\u0026ldquo;Smartphone Application for Tracking Students\u0026rsquo; Class Attendance,\u0026rdquo; \u003cem\u003eSISY 2016 IEEE 14th International Symposium on Intelligent Systems and Informatics\u0026nbsp;\u003c/em\u003eAugust, 2016: 227\u0026ndash;232.\u003c/li\u003e\n \u003cli\u003eJ. D. Sweetlin, V. Aswini, and R. Dhanusha, \u0026ldquo;Speech Based Attendance Application Register,\u0026rdquo; \u003cem\u003eFifth International Conference On Recent Trends In Information Technology\u003c/em\u003e, 2016:1-5.\u003c/li\u003e\n \u003cli\u003eP. Wagh, \u0026ldquo;Attendance System based on Face Recognition using Eigen face and peA Algorithms,\u0026rdquo; \u003cem\u003eInternational Conference on Green Computing and Internet\u0026nbsp;of Things (ICGCIoT),\u0026nbsp;\u003c/em\u003e2015: 303\u0026ndash;308.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"Vidyalankar Institute of Technology ","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Face Recognition, Smart Attendance System, Open CV, Numpy","lastPublishedDoi":"10.21203/rs.3.rs-4396816/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4396816/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eFace Recognition based attendance is a modern- day technique to take attendance. It can be used in various places such as schools, offices, parliaments, etc. The main aim of this system is to enhance and upgrade the present attendance system which would be biometric r signature based. The current system has a lot of faults and inaccurate in taking attendance and has various disadvantages. This is the reason behind face recognition- based attendance system. We are using the natural traits of human body that is human face. As each has unique and different identity therefore the possibility to deviate from the result is very low\u003c/p\u003e","manuscriptTitle":"Face Recognition Based Smart Attendance System","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-05-13 05:50:12","doi":"10.21203/rs.3.rs-4396816/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"5310f08f-71ff-4fbc-8fbf-bbe901569c26","owner":[],"postedDate":"May 13th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":31735012,"name":"Artificial Intelligence and Machine Learning"}],"tags":[],"updatedAt":"2024-05-13T05:50:12+00:00","versionOfRecord":[],"versionCreatedAt":"2024-05-13 05:50:12","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4396816","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4396816","identity":"rs-4396816","version":["v1"]},"buildId":"7rjqhiLT3MXkJMwkYKINL","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}