MTCNN and FACENET-Based Face Detection and Recognition Model for Attendance Monitoring

Attendance monitoring with the help of face recognition is an emerging technology that uses artificial intelligence and computer vision to automate attendance tracking in various scenarios. The system captures an individual’s face image and compares it with the stored data in the database to confirm the identity of the person. The literature survey shows that there are lots of algorithms available for use, but Multi-task Cascaded Convolutional Neural Network (MTCNN) is the best possible one (Chumming and Ying in Autom Control Comput Sci 55(1), 102–112, 2021). This paper also proposes an improved Face Net-based access control mechanism that improves the loss function that has application in facial recognition and face verification obtained with a high degree of accuracy. Then, the performance of the proposed model will be compared with the all-preexisting models. The experimental results with this FaceNet-based MTCNN model meet the requirements of the recognition in real time, and with this proposed model, recognition accuracy reaches 99.87%.