Real-time Triplet Loss Embedding Face Recognition for Authentication Student Attendance Records System Framework

Abstract

The number of times students attend lectures has been identified as one of many success factors in the learning process in many studies. We proposed a framework of the student attendance system by using face recognition as authentication. Triplet loss embedding in FaceNet is suitable for face recognition systems because the architecture has high accuracy, quite lightweight, and easy to implement in the real-time face recognition system. In our research, triplet loss embedding shows good performance in terms of the ability to recognize faces. It can also be used for real-time face recognition for the authentication process in the attendance recording system that uses RFID. In our study, the performance for face recognition using k-NN and SVM classification methods achieved results of 96.2 +/- 0.1% and 95.2 +/- 0.1% accordingly. Attendance recording systems using face recognition as an authentication process will increase student attendance in lectures. The system should be difficult to be faked; the system will validate the user or student using RFID cards using facial biometric marks. Finally, students will always be present in lectures, which in turn will improve the quality of the existing education process. The outcome can be changed in the future by using a high-resolution camera. A face recognition system with facial expression recognition can be added to improve the authentication process. For better results, users are required to perform an expression instructed by face recognition using a database and the YOLO process.

Facial Recognition Technology-Based Attendance Management System Application in Smart Classroom

“…However, this method is less accurate when used simultaneously on multiple databases and groups of students. Another example of deep learning in action with Viola Jones is shown in [21]. Viola-Jones was used to identify the faces, and the database was created.…”

“…Triplet selection: To expedite learning, triplets that violate the above equation are chosen. The highest possible () p i E x values and the lowest possible () N i E x values are selected for each () a i E x [47]. Because completing this procedure on all datasets is mathematically tricky, the triplets are chosen in one of two ways:…”

Max Feature Map CNN with Support Vector Guided Softmax for Face Recognition

“…They improved the model by using a tripled-based metric approach that is similar to FaceNet. Other researchers also studied the triplet loss's effectiveness combined with k-NN and SVM classifier for face recognition and achieved 96% and 95% accuracy, respectively [22].…”