FACIAL RECOGNITION USING THE FACENET MODEL.

Abstract

This thesis delves into the realm of facial recognition, focusing on the implementation of the Facenet model. The research problem addresses the efficacy of the Facenet model in accurately recognizing and identifying faces in various settings and conditions. Methodologically, the study employs deep learning techniques and neural networks to train and evaluate the Facenet model's performance in facial recognition tasks. The main results show the model's commendable accuracy in identifying faces on the personal dataset I collected and under different environmental conditions. Furthermore, the thesis discusses the model's strengths in handling variations in pose, illumination, and facial expressions, contributing to its robustness. In conclusion, the study underscores the effectiveness of the Facenet model in facial recognition tasks, highlighting its potential applications in security, surveillance, and personal identification systems.