A Deep Learning Approach to Face Detection and Recognition

Abstract

Facial recognition technologies, a key component of biometric detection systems, are increasingly adopted across multiple sectors due to their reliability in authentication and user-friendly application. These systems typically operate in three main phases: face detection, image preprocessing, and face recognition, leveraging distinctive physical or behavioral traits for identification. Traditional methods often encounter challenges in real-world environments with variations in lighting, facial pose, and expressions. This study presents a robust framework for face detection and recognition, employing the Viola-Jones algorithm for initial detection, followed by a deep learning model for feature extraction and recognition. Our approach is designed to enhance accuracy on the Face database, effectively managing the challenges posed by diverse facial features and environmental variations. Experimental results show that our proposed system surpasses traditional face recognition methods, thereby contributing to improved system performance in complex settings.