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AUTOMATION OF ATTENDANCE USING DEEP LEARNING
1.
International Research Journal
of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 09 Issue: 11 | Nov 2022 www.irjet.net p-ISSN: 2395-0072 © 2022, IRJET | Impact Factor value: 7.529 | ISO 9001:2008 Certified Journal | Page 572 AUTOMATION OF ATTENDANCE USING DEEP LEARNING Chiluka Harshith Reddy1, Lavu Nithish Chandra2, Ranabothu Sri Anand Reddy3 1,2,3Student, SENSE, VIT Vellore, Tamil Nadu, India ---------------------------------------------------------------------***--------------------------------------------------------------------- Abstract - Student Attendance mainframe structure is defined for managing the student's class attendance data files using the concept of face detection and recognition through open computer vision.This approach is proposed primarily to improve the existing university attendance practices and prevent the waste of resources and time. The concept of moving from the traditional attendance system to the digital one using face detection and recognition techniques has been driven by the automation world's pointing sides.Byaddingthe dataset of an individual, the Student Attendance structure is constructed in this way. In addition to lowering the long-term time burden work, and disposables required, the main goal of constructing this system was to increase the adaptability and performance of the attendance system procedure.TheStudent Attendance markup structure's primary functionistoaddand modify a student's attendance notes, make an automatic computation of the number of presentees and absentees depending on the subject and affability of the class, and then produce an automated document or spreadsheet.The concept of open computer vision is used in this approach, which is entirely based on the general-purpose language Python.For face detection system we used haarcascade and for face recognition, we used LBPH model;After training each individual student, the system generated a spreadsheet that included the number of students present in the classroom along with a picture or video that was captured live. Key Words: Principal Component Analysis, SVM, Dlib, LBP Feature, API, Tensorflow 1. INTRODUCTION Maintaining attendance is crucial at any institute for monitoring the quality of education. Students' attendance is routinely recorded by establishing attendance files or notes provided by the departmental arch in the depths of the institutions. The teacher manually takes attendance by calling out each student's name and confirming whether or not they are present in the class. This process is dull, time- consuming, and unreliable because students frequently make the wrong calls for their absent friends. Additionally, this procedure makes it more difficult to alter every student's attendance in a large classroom. In order to automatically identify the students in a class and record their attendance by collecting their frames, we designedthis application and used a range of techniques, including facial exposure and an understanding system. While some biotech assimilation metrics can be increased properly, in the past, students typically had to wait longer when they entered the room for attendance. Face recognition is the best option because of its non-intrusiveness and familiarity, as people generally know other people by their facial features. This facial biometric structure primarily consists of an enrolled approach in which, following the process of detecting and understanding, the key distinguishingcharacteristicsofeach individual face will be saved in the dataset. The traditional methods for analyzing student engagement in particular subjects involve physically signing the attendance logs in a PC framework for analysis. This approach is ineffective because students would sign up for their absent classmates, which is boring, unpleasant, and prone to errors. The use of the face identification and acknowledgment framework in place of the conventional methodswill providea quickerand more effective method for accurately capturing student participation while also providing secure, reliable, and strong restrictions on the framework records. After these restrictions have been approved, one can access the records for any purpose, including for organization,guardianship, or even for the student's own studies. 2. LITERATURE REVIEW 2.1 Face Recognition: From Traditional to Deep Learning Methods Early face recognition research centered on techniques that matched basic features using image processing algorithms de-tracing the faces' geometrical shapes.Nevertheless,these techniques only functioned in incredibly restricted circumstances,theydemonstrated.Computersarecapableof recognizing faces automatically. Then, statistical sub spaces techniques like principal component linear discriminant analysis with principal component analysis (LDA) grew in acceptance. These techniques are known as holistic because they incorporate data from the full-face region. During this time, advancements in other computervisionfieldsledtothe creation of capable local feature extractorsthatelucidate the texture of an image in several areas. Feature-basedmethods for facial recognition involve comparing these regional specifics over pictures of faces. Hybrid methods were created by further developing and combining holistic and feature-based approaches. Facerecognitionsoftwareusinga combination of techniques Up until recently,state-of-the-art technology was deep learning the most effective strategyfor most computer vision applications, such as facial identification. The remainder of this text summarizes some of the most notable reviews Each of the aforementioned kinds of strategies is supported by a search.
2.
International Research Journal
of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 09 Issue: 11 | Nov 2022 www.irjet.net p-ISSN: 2395-0072 © 2022, IRJET | Impact Factor value: 7.529 | ISO 9001:2008 Certified Journal | Page 573 2.2 Class Attendance System Using Viola-Jones Algorithm and Principal Component Analysis The outcome in Viola and Jones depends on the data and unreliable classifiers. The uniformity of thetrainingsethasa significant impact on the final detection's quality. Important considerations include the size of the sets and the inter class variability. When numerous peoplewithdifferentsequences are considered, the analysis yields very poor results. Viola and Jones algorithm is the employed algorithm inthispaper. The data's training process should be carriedoutproperlyin such a way that the quality of the final detection increase. The system overview ought to include the overall building design that will the concise and thorough details about the project. 2.3 Automatic attendance system using Deep Learning The system is put into practice under the basic and fundamental tenet of a digital camera in the classroom. In a lecture that lasted 50 minutes, the digital camera wouldtake 2 pictures every 25 minutes. The system will now get the image and extract all of the faces from it. Now, the existence of the face would be determined by comparison with the trained model of faces already in use. If a student's face is in the current database, the system will save their unique ID in the attendance database or discards them if the studentisn't in the database. Student database We havetackleda number of issues in this study, including real-time face detection, multiple face detection, and integration with the computer learning algorithm. The actual challenge in putting an idea into practice was real-time face extraction from an image. In order to resolve this problem, we employed using the Deep Neural Network (DNN) of the Tensorflow estimator API, which is also trained from the instantaneously extracted photos. However, finding face-like patterns is only a small portion of the issue. You must use face recognition technology using an algorithm to successfully identify a student from a database of pupils. We usedGoogle'sface net, a model that has been pre-trained on 150 000 photos and was inspired by the Google Pixel, to deal with this problem. 2.4 Face Recognition Using Neural Network The computation is slow and the detecting method is complicated. Performance compared to the Viola-Jones algorithm is typically worse. The algorithm is neural network-based. This strategy is onlyeffectiveifthelarge size of the image was taught. 3. IMPLEMENTATION 3.1 Existing System In the last few years, face recognition technology has embraced a wide range of methodologies, but the classical methodology still predominates. Component analysis, discriminating analysis, discrete transformation, and component analysis are categories for prestigious face recognition. It is considered to be the most important element in facial recognition technology. Numerous researchers in the field of facial recognition technologies employ the eigenfaces technique. The main element of this technique is eigenfaces. Basically,itdivideda varietyofinput variables into several classes (Li & Hua, 2015). The PCA (Principal Component Analysis) algorithm can be used to extract the image data in its original form. One of the fundamental principles that PCA adheres to is that it can recreate the image's original form from the original collection by using eigenfaces. In face recognition technology, Eigenfaces are regarded as the key component. Eigenfaces typically depict the primaryfacial characteristics, which the original image may not have had. 3.2 Proposed System An attempt is made to develop the automated facial attendance system utilizing SVM on the LBP feature taking into account the drawbacks of some of the systems listed in the previous works, as the LBP method provides good accuracy in comparison to other systems. The suggested method introduces an automated attendance system that incorporates a facial recognition algorithm and an Android app. Any device with a camera is capable of taking a picture or a video, which it may then upload usinga webapplication. The received file is put through face detection and recognition processes, so the detected faces are extracted from the image. 3.3 Gap Identified A few drawbacks of facial recognition areimagequality,size, angle of facing, and processing time. performance of the facial recognition algorithmisfirstfundamentallyinfluenced by the image quality. When comparedtoa digital camera, the video scanning of an image has inferior quality. The method of facial detection as a whole was impacted by the image quality. Face recognition poses considerable challenges in terms of storage and processing. To recognize a person's true appearance, a certain angle is chosen ( Minaee & Wang, 2015). The process of detecting faces will be severely hampered by the several angles that must be used in order to obtain an adequate face while employing recognition software. In essence, they adopted the 2D facial type'sphoto structure. This format prevents facial recognition software from currently detecting numerous faces. The facial recognition technology will experience issuesbecauseofthe person's movements, which resulted in erroneous photographs being captured. It is necessary to use current software, which is highly expensive on the market, for more accuracy. The detection method can occasionally run into trouble when the photos are blurry. The effectiveness of facial recognition technology is also influenced by the camera's perspective.
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International Research Journal
of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 09 Issue: 11 | Nov 2022 www.irjet.net p-ISSN: 2395-0072 © 2022, IRJET | Impact Factor value: 7.529 | ISO 9001:2008 Certified Journal | Page 574 3.4 Principal Component Analysis (PCA) It is derived from the transition of Karhunen-Loeve. Principal Component Analysis (PCA) often locates a t- dimensional subspacewhosebasisvectorscorrespondto the largest variance directionin theoriginal imagespacegivinga dimensional vector representation of each face in a training set of photos. Typically, this new subspace has a lower dimension (ts). The PCA basis vectors are defined as eigenvectors of the scatter matrix if the image elements are thought of as random variables. For dimensionality reduction, the Eigenface technique use PCA to identify the vectors that best capture the distribution of face pictures throughout the whole image space. The subspace of face images is defined by these vectors, and it is known as face space. To determine a set of weights that accurately represents the contribution of each vector in the face space, all of the faces in the training set are projected onto the face space. To generate the appropriate set of weights for identifying a test picture, the test image must be projected into the face space. The face in the test image can be recognized by comparing the weights of the test image with the set of weights of the faces in the training set. The foundation of PCA's primary methodistheKarhumen-Loeve transformation. The image might be viewed as a sample of a stochastic process if the image's constituent parts are assumed to represent random variables.Theeigenvectorsof the scatter matrix ST, ST=Σ N i=1 (xi - μ)(xi - μ)T, are what are known as the PCA basis vectors. 4. SYSTEM OVERVIEW The ultimate objective of a face recognition system is image understanding, or the ability to recognize an image's meaning in addition to its structure. A general definition of automatic face recognition is as follows:givenstill ormoving photographs of a scene, identify or confirm one or more people in the scene using a database of faces that have as been previously recorded. The challenge can be solved by segmenting faces (facial detection) from cluttered scenes, extracting features from the face regions, and then identifying or verifying the faces. The inputforidentification is an unidentified face, and the system returns the identity it has deduced from a database ofwell-knownpeople, whereas in issues with verification: the system must accept or reject the claim of the input's identity. 4.1 Data Set Creation 1. Obtain the class's student roster. 2. Look through each image folder associated with each student. 3. The parent folder name appears as the class label for each image. 4. Determine whether a face is there by applying a face detection algorithm to the face. 5. Get the bounding box coordinates if a face is found. 6. Crop the image so that it only contains a face using the bounding box coordinates. 7. Use Dlib to determine the direction of the face, then apply transforms to align the eyes, mouth, andotherfacial features at a specific angle. This guarantees the neural network's input is of high quality. 8. Feed the neural network with the aligned image. The network produces 128 measurements of each face. 9. At this point, the image's data have been extracted.Traina classifier using this embedding vector. 10. The classifier is finished off and saved asanobject,which can later be imported from the database and used to make predictions. 4.2 Applying the LBP operation The initial computational stage of the LBPH is to produce an intermediate image that, by emphasizing thefacefeatures of the original image, more accurately describes the original image. The method does this by utilizing a sliding window idea depending on radius and neighbour. The procedure is shown in the image.
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International Research Journal
of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 09 Issue: 11 | Nov 2022 www.irjet.net p-ISSN: 2395-0072 © 2022, IRJET | Impact Factor value: 7.529 | ISO 9001:2008 Certified Journal | Page 575 5. EXPERIMENTAL RESULTS 6. CONCLUSION This method has been suggested to keep up attendance. The paperwork and stationery are replaced with an automated system that is quick, effective, cost-effective, and time- saving. The proposed system, however, is anticipated to produce the intended outcomes. Integrating other effective methods could also increase efficiency. Here, we've covered a variety of face identification techniquesthattheresearcher utilized. These techniques might be applied by educational or business institutions to track students' attendance at lectures by identifying their faces. In order to classify faces, we are attempting to develop a system using Improved Support Vector Machines (IVSM) on LBP features in the following phase. FUTURE DEVELOPMENT In the future, we'll connect the system with the email address and mobile number so that if anybody doesn'tshow up, an automatic SMS or MAIL will be sent to them. REFERENCES [1] P. Mehta, “An Efficient Attendance Management System based on Face Recognition using Matlab and Raspberry Pi 2,” Int. J. Eng. Technol. Sci. Res. IJETSR, vol. 3,no.5,pp. 71–78, 2016.. [2] Yang Zhong Josephine Sullivan Haibo L KTH Royal Institute of Technology, 100 44 Stockholm, Sweden "Face Attribute Prediction [3] Max Ehrlich, Timothy J. Shields, Timur Almaev, and Mohamed R. Amer, "Facial Attributes Classification using Multi-Task Representation Learning",2016 IEEE Conference on ComputerVisionandPatternRecognition Workshops. [4] Yun-Fu Liu, Member, IEEE, Jing-Ming Guo, Senior Member, IEEE, Po-Hsien Liu, Jiann-Der Lee, and Chen- Chieh Yao, "Panoramic Face Recognition", IEEE, Transactions on Circuits and Systems for Video Technology. [5] Hamdi Dibeklio˘glu, Member, IEEE, Fares Alnajar, Student Member, IEEE, Albert Ali Salah, Member, IEEE, and Theo Gevers, Member, IEEE, "Combining Facial Dynamics With Appearance for Age Estimation", IEEE TRANSACTIONSON IMAGEPROCESSING,VOL.24,NO.6, JUNE 2015. [6] W. Haider, H. Bashir, A. Sharif, I. Sharif, and A. Wahab,“A Survey on Face Detection and Recognition Techniques,” Res. J. Recent Sci., 2014. [7] I. Dagher, “Incremental PCA-LDA algorithm,” in CIMSA 2010 - IEEE International Conference onComputational
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International Research Journal
of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 09 Issue: 11 | Nov 2022 www.irjet.net p-ISSN: 2395-0072 © 2022, IRJET | Impact Factor value: 7.529 | ISO 9001:2008 Certified Journal | Page 576 Intelligence for MeasurementSystemsandApplications, Proceedings, 2010. [8] N. Kar, M. K. Debbarma, A. Saha, and D. R. Pal, “Study of ImplementingAutomatedAttendanceSystemUsingFace Recognition Technique,” Int. J. Comput. Commun. Eng., vol. 1, no. 2, pp. 100–103, 2012.
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