Video-Based Dynamic Human Authentication System Using Facial Recognition

Abstract

Advancements in technology have made information security an indispensable aspect of modern systems. As data breaches and identity theft become more prevalent, the need for effective authentication methods is crucial. This paper presents a human authentication system based on facial video analysis, emphasizing its advantages over traditional methods. By utilizing Python libraries, including Haar cascade for face detection and Convolutional Neural Networks (CNN) for feature extraction, our system captures dynamic facial movements to enhance accuracy and security. Experimental results indicate a significant improvement in recognition rates, showcasing the potential for real-world applications in various security domains.