Design of Face Recognition Course Checking System

Abstract

This paper proposes a deep learning based facial recognition course checking system to address the issues of low attendance efficiency and easy proxy signing in traditional universities. Extracting 128 dimensional facial feature vectors through the MobileFaceNet lightweight model, combined with the Siamese network's dual tower structure similarity measurement mechanism, implementing identity recognition with a Euclidean distance threshold of 0.5, and introducing liveness detection to prevent image spoofing. The experiment was based on the CASIA and MS1M-V2 datasets, and the results showed that the recognition accuracy of MobileFaceNet alone reached 98.6%. After 100 iterations of integrating Siamese network, the accuracy improved to 100%. The model file was only 65KB, which meets the real-time update and lightweight requirements of embedded devices. This system can achieve non-contact and efficient attendance, support cross campus management and academic analysis related to attendance data. In the future, we plan to optimize model compression technology and integrate multimodal biometric features to expand to scenarios such as enterprise attendance and security access control.