Smart Attendance Management System Based on Face Recognition Using CNN
Syam Kakarla, Priyaranjan Gangula, M.Sai Rahul, Chetanpal Singh, T. Hitendra Sarma
Abstract
Convolutional Neural Networks has been playing a significant role in many applications including surveillance, object detection, object tracking, etc. Extensive research is recorded for face recognition using CNNs, which is a key aspect of surveillance applications. In most recent times, the Face Recognition technique is widely used in University automation systems, Smart Entry management systems, etc. In this paper, a novel CNN architecture for face recognition system is proposed including the process of collecting face data of students. Experimentally it is shown that the proposed CNN architecture provides 99% accuracy. Further, the proposed CNN framework is used to develop a “Smart Attendance Management System (SAMS)“, which is a web-based application, to provide attendance of students using face recognition, in realtime. The proposed application is easy to deploy and maintain.