Vision-Face Recognition Attendance Monitoring System for Surveillance using Deep Learning Technology and Computer Vision

Abstract

Nowadays, Artificial Neural networks can be trained over several billion images and can be used to detect and recognize faces with relative ease and flexibility in an instant. This concept is used in the implementation of this real time attendance cum surveillance system that can be prototyped and set into action. Some of the major applications of this innovative method include face attendance using a single snap mode in smartphones for university classes, further real-time facial recognition surveillance of lab facilities and work places which can set this as a first line of defense against intruders from gaining access. The user-friendly graphical user interface provides flexibility and ease in running these powerful face recognition algorithms powered by deep-learning. We have achieved a maximum recognition accuracy of 74 percent while running the real time surveillance algorithm. This work was done as a solution to the absence of a robust and user friendly face recognition attendance system.