Implementing an attendance system based on facial recognition technology using artificial intelligence technology

Abstract

In light of technological progress and the significant development in the field of artificial intelligence, it has become necessary to apply these technologies in various practical fields, especially in the educational sector in Middle Eastern universities, which still face constraints in technological infrastructure. To address this problem, deep learning–based AI algorithms were used to perform face detection and recognition. These algorithms have been applied to detect and record attendance, and provide daily, weekly and monthly reports. The system uses an application and comparison of the results of applying the MTCNN algorithm for face detection with the FaceNet algorithm for face recognition. The other aspect represents the application of the RetinaFace algorithm with the ArcFace algorithm, which is characterized by its accuracy and efficiency in dealing with difficult circumstances. Finally it was merged

RetinaFace for face detection and MTCNN for facial feature alignment and enhancement, followed by ArcFace for accurate facial recognition. The hybrid combination of these three algorithms proved the accuracy and efficiency of the second algorithm, RetinaFace, with the ArcFace algorithm, because the system gave the same results when applied alone or when combined. Thus, when comparing the results of the three operations, the RetinaFace algorithm with ArcFace proved its superiority over the rest of the proposed algorithms. The system was also designed to work under different environmental conditions and in educational institutions with limited technological infrastructure.