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.
