Face Recognition-Based Automated Attendance System for Educational Institutions Utilizing Machine Learning

A comparative study on local binary pattern (LBP) based face recognition: LBP histogram versus LBP image

Face recognition is gaining popularity as one of the biometrics methods for an attendance system in an organization. Due to the pandemic, the common face recognition system needs to be modified to meet the current needs, whereby facemask detection is necessary. The main objective of this paper is to investigate and develop a real-time face recognition system for the attendance system based on the current scenarios. The proposed framework consists of face detection, mask detection, face recognition, and attendance report generation modules. The face and facemask detection is performed using the haar cascade classifier. Two techniques for face recognition were investigated, the eigenfaces and local binary pattern histogram. The initial experimental results and implementation at Kuching Community College show the effectiveness of the system. For future work, an approach that is able to perform masked face recognition will be investigated.

The Face -Based Attendance System is an innovative solution designed to automate and streamline the process of recording attendance in academic institutions and workplaces. Traditional methods, such as manual sign-ins and RFID-based systems, are often time-consuming and susceptible to proxy attendance. This project leverages advanced computer vision and machine learning techniques, particularly using Open CV and the Haar Cascade Classifier, to detect and recognize faces in real time. The system captures live video input through a webcam, processes the facial features, and matches them with stored profiles to mark attendance automatically. This approach not only improves accuracy and efficiency but also enhances security and reduces administrative overhead. The system is implemented using Python and integrates a graphical user interface (GUI) for ease of use. Overall, this project demonstrates the potential of facial recognition technology in creating smart, secure, and contact-less attendance systems. Keywords: Face recognition, attendance system, computer vision, machine learning, OpenCV, Haar Cascade Classifier, real-time detection, Python, automation, contactless attendance.

Achieving low recognition time and high accuracy in real-time face recognition is challenging. This study implements Viola-Jones, Local Binary Pattern Histogram, and Euclidean Distance for real-time face recognition and calculates the face detection time. The face image is detected using the Viola-Jones method; its features are extracted using the Local Binary Pattern Histogram, and the face is recognized using Euclidean Distance. This study processes sample images from 1013 students as training data, with 20 images represent each student. The experiments show that 268 of 342 testing data are recognized correctly, resulting in an accuracy of 78.4%, with average real-time recognition time of 0.93 seconds.

In real world classroom settings, traditional classroom attendance methods are often time consuming, prone to human errors, and inefficient in terms of accuracy. Different solutions have been introduced from time to time, to make them easy, accurate and automatic. Use of computer vision and face recognition is gaining momentum as it provides automation and increases the precision and effectiveness of attendance system. The research aims to design automated attendance systems capable of working seamlessly in real classroom settings, focusing on increasing the accuracy as well as reliability for the development of automated attendance system. With the goal of providing effective fast automatic classroom attendance, this research focusses on evaluating performance of different Convolutional Neural Network architectures performance, such as VGGNet, ResNet, and DenseNet. For the evaluation of the performance of these Convolutional Neural Networks, we have used parameters like accuracy, precision, recall, and F1 score for a real world classroom scenario with different lighting conditions, poses, and occlusions. Our research shows that the ResNet performs better with respect to speed and accuracy in comparison to VGGNet and DenseNet. This makes ResNet an optimum choice for real time deployment.

Due to a large number of students in the schools and colleges most of the time teachers are not able to monitor on the attendance of the students. This may create the problems to both students and teachers in the end of the year or semester. The manual handling of all these can take much more effort in making the students to attend the classes regularly and also to intimate their parents on the valid interval of time. So to make this system more efficient and thereby to increase the productivity of the college or the school proposed system uses the automatic attendance system, where the proposed model takes the input from the staff regarding the attendance details and facial images of the students, then the system trains a Convolutional Neural Networks that have been deployed specifically for the facial recognition purpose. This trained model is then utilized for the purpose of achieving the identification of the student’s faces through the test image and the decision making approaches for the marking of the attendance. The methodology has been subjected to the evaluation for the accuracy of facial recognition which has resulted in highly accurate outcomes.

Investment in facial recognition technologies has increased recently with the amount of venture capital invested in facial recognition startups dramatically increasing in 2021. Facial recognition uses AI and ML techniques to find human faces in the surrounding area. Facial recognition technology is used by the web application Automated Attendance System (AAS) which was developed by a group of students from the University of Wollongong in Dubai to automate attendance management in educational institutions. AAS is simple to use, quick to implement, and can be incorporated into current educational institutions. Deep convolutional neural networks, notably the VGG19 and EfficientNetB0 models, are the foundation of the system. These models were trained for high accuracy utilizing transfer learning and ensemble learning. The automation of attendance tracking reduces human error; increases efficiency, accuracy, and integrity; and does away with the need for manual methods of collecting attendance.

The face recognition (FR) process identifies or confirms a person with the help of images and videos. The traditional aging FR (AFR) method encounters numerous issues as a result of age-related biological transformation. An adaptive fully recurrent deep neural learning (AFRDNL) technique is presented in this research to improve FR accuracy (FRA) with minimal time complexity (TC). Feature extraction and classification are two processes included in the proposed technique. The AFRDNL technique gathers facial images from the database and trains the face images with three layers. To learn the facial features, three hidden layers are employed in the AFRDNL technique. Finally, the AFRDNL technique utilizes the Gaussian activation function for matching extracted testing features with earlier saved training features. This process is repeated until a minimal training error with a higher FRA is achieved. Experimental evaluation is carried out with three image datasets like Face and Gesture Recognition Research Network, MORPH and Cross-Age Labeled faces in the Wild (CALFW) datasets using different metrics, such as FRA, false-positive rate (FPR), TC and F1score. The experimental results confirm that the AFRDNL technique effectively increases the FRA and F1score and minimizes the FPR and TC more effectively than the state-of-the-art methods.

Daily attendance marking is a common and important activity in schools and colleges for checking the performance of students. Manual Attendance maintaining is difficult to process, especially for a large group of students. Some automated systems developed to overcome these difficulties, have drawbacks like cost, fake attendance, accuracy, intrusiveness. To overcome these drawbacks, there is a need for a smart and automated attendance system. Traditional face recognition systems employ methods to identify a face from the given input but the results are not usually accurate and precise as desired. The system described in this we aim to deviate from such traditional systems and introduce a new approach to identify a student using a face recognition system, the generation of a facial Model. This describes the working of the face recognition system that will be deployed as an Automated Attendance System in a classroom environment. The proposed smart classroom system was tested for a classroom with 20 students at K L University Andhra Pradesh, Vijayawada, India and we got the experimental results to demonstrate the train and test accuracy of 97.67% and 96.66%, respectively. In this paper we selecting of the face recognition and detection giving result using Python language in PYCHARM tool. This requires high end specifications of a system in order to get better results. It won’t run on all the small specification systems. So, this can run only a small database and compare them with the face required.

The attendance application is one important system that must be possessed by a school in order to manage student attendance data. Some schools still use manual methods for student attendance by using signatures or calling them one by one. Manual attendance is considered inefficient because the risk of losing attendance sheets is very easy and parents cannot monitor whether their child is attending school or not. This is directly felt by SMA Dua Mei Ciputat with the presence of students who left home but did not attend school. Therefore, this research aims to assist teachers and parents in monitoring students to be more disciplined in coming to school by building a face recognition application for attendance using the haar-cascade classifier method based on a website. The haar-cascade classifier method has the advantages of computation in face detection and the local binary pattern histogram algorithm which has good accuracy in face recognition. The results of this research show that the haar-cascade classifier method can be used to create a face recognition application for attendance with fairly good accuracy, namely 100% with 5 attempts with recognition of one face, 100% with 5 attempts with recognition of two faces, and 20% with 5 attempts with recognition of three faces. With these results, the application is only capable of recognizing two faces simultaneously in the attendance process.

This research aims to develop artificial intelligence (AI) algorithms in the context of facial recognition with a focus on increasing accuracy in difficult environmental conditions. Although facial recognition technology has made great progress, challenges such as poor lighting, variations in facial expressions, and head rotation are still problems that must be overcome. The research methodology involved collecting a wide dataset covering a wide variety of faces under various environmental conditions. This data is then processed and its features are extracted using computer image processing techniques. Furthermore, several deep neural network architectures, such as Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), were developed, trained, and evaluated for face recognition tasks. The expected result is the development of an AI algorithm that is able to overcome challenges in facial recognition with higher accuracy than existing methods. In particular, significant improvements in facial recognition accuracy are expected especially under low lighting conditions and variations in facial expressions. This research has a major impact in a variety of security applications, such as border surveillance, building access control, and corporate security. With higher facial recognition accuracy, security risks can be significantly reduced, resulting in safer and more efficient security solutions. In conclusion, this research aims to bring innovation in facial recognition technology through advanced AI approaches, with the potential to improve security in various contexts.

Abstract— In this digital era, face recognition system plays a vital role in almost every sector. Face recognition is one of the mostly used biometrics. It can used for security, authentication, identification, and has got many more advantages. Despite of having low accuracy when compared to iris recognition and fingerprint recognition, it is being widely used due to its contactless and non-invasive process. Furthermore, face recognition system can also be used for attendance marking in schools, colleges, offices, etc. This system aims to build a class attendance system which uses the concept of face recognition as existing manual attendance system is time consuming and cumbersome to maintain. And there may be chances of proxy attendance. Thus, the need for this system increases. This system consists of four phases- database creation, face detection, face recognition, attendance updation. Database is created by the images of the students in class. Face detection and recognition is performed using Haar-Cascade classifier and Local Binary Pattern Histogram algorithm respectively. Faces are detected and recognized from live streaming video of the classroom. Keywords—Face Recognition; Face Detection; Haar-Cascade; Opencv :library are used in face recognition system.

There has been much research on computer vision in last three decades. Computer vision methods have been developed for different situations. One example is a detection of human face. For computers face detection is hard. Faces look different from different viewing directions. Facial expressions affect to the look of the face. Each individual person has a unique face. The lightning conditions can vary and so on.However, face detection is currently possible in limited conditions. In addition, there are some methods that can be used for gender recognition [3], face recognition [5] and facial expression recognition [2].Nonetheless, there has been very little research on how to combine these methods. There has also been quite little research on how to apply these methods in human-computer interaction (HCI).Finding sets of techniques that complement each other in a useful way is one research challenge. There are some applications that take advantage of one or two computer vision techniques. For example, Christian and Avery [1] have developed an information kiosk that uses computer vision to detect potential users from a distance. A similar kiosk has been developed by us in the University of Tampere [4]. There are also some games that use simple computer vision techniques for the interaction. However, there are very few applications that use several computer vision techniques together such as face detection, facial expression recognition and gender recognition. Overall, there has been very little effort in combining different techniques.In my research I develop computer vision methods and combine them, so that the combined method can detect face, recognize gender and facial expressions. After successfully combining the methods, it is easier to develop HCI applications that take advantage of computer vision. Applications that can be used by small group of people are my specific interest. These applications allow me to build adaptive user interfaces and analyze the use of computer vision techniques in improving human-computer interaction.

    The IoT Based Automated Attendance System was designed and developed to make an efficient contactless attendance marking system with a mobile application feature. This study aimed to improve the traditional attendance log system and contact fingerprint attendance within an institution through the use of facial recognition. The study used the OpenCV and Python Programming in order to create the facial recognition program, while the mobile application was developed through the use of Visual Studio Code and Android Studio. The prototype was composed of mini pc which handled all the processes the device had to execute. The computer was used to interface other components of the device such as webcam, monitor, keyboard, and mouse. Since this is an automated attendance system, portability and efficiency were given importance. The prototype’s performance was tested and monitored through the use of 100 participants including the researchers themselves. The project was tested by the Department of Computer, Electronics, and Electrical Engineering (DCEE) at the Engineering Science Building, Cavite State University, Indang, Cavite with the Electronics Engineer Professors. Based on the results of the evaluation, the design project succeeded in achieving its objectives. The overall performance was considered satisfactory. The cost computation of the system amounted to PHP 14,150.00.  

Taking attendance in a large class is cumbersome, repetitive, and it consumes valuable class time. To avoid these problems, we propose an automatic attendance system using deep learning framework. An automatic attendance system based on the image processing consists of two steps: face detection and face recognition. Face detection and recognition are well-explored problems in computer vision domain, though they are still not solved due to large pose variations, different illumination conditions, and occlusions. In this work, we used state-of-the-art face detection model to detect the faces and a novel recognition architecture to recognize faces. The proposed face verification network is shallower than the state-of-the-art networks and it has achieved similar face recognition performance. we achieved 98.67% on LFW and 100% on classroom data. The classroom data was made by us for practical implementation of the complete network during this work.