Real Time Face Recognition Based Attendance System using Multi Task Cascaded Convolutional Neural Network

Facial recognition has been an important research direction in computer vision. There are countless algorithms presented in related disciplines, and the precision that may be achieved is increasing. However, the implementation of facial recognition technology is hard. In this paper, combination of facial recognition and facial recognition algorithms to build a video-based facial recognition system to efficiently and accurately mark participant attendance. Utilizing FaceNet to extract characteristics and use MTCNN to detect the image of the student for recognition. Lastly, the output is analyzed by a Support Vector Machine (SVM) that recognizes the person of interest in the image. Studies reveal that this technique still yields accurate detection results when the dependent variable has no data and the image quality is unreliable. On the self-generated data set used in this article, the accuracy of the procedure may reach 94.85%.