In the contemporary era of digital transformation, secure and automated identification systems have emerged as critical components of human-computer interaction frameworks. This paper presents a comprehensive web-based face recognition system developed using the Flask framework and OpenCV library in Python. The proposed system leverages state-of-the-art deep learning techniques to perform real-time detection and recognition of human faces with high accuracy. By integrating machine learning models for facial feature encoding and comparison, the system enables biometric authentication that eliminates reliance on traditional password-based mechanisms. The application provides an intuitive web interface that facilitates user registration and identity verification through live camera input. Experimental results demonstrate that the system achieves recognition accuracy exceeding 95% under optimal conditions, with robust performance across varying lighting scenarios. The lightweight architecture enables straightforward deployment on standard web servers, making the solution viable for diverse applications including attendance monitoring, access control systems, and identity verification platforms in both academic and enterprise environments.
Kaviya et al. (Wed,) studied this question.