Abstract

Facial recognition technology has gained immense popularity in recent years due to its applications in security, authentication, and personalized user experiences. Traditional facial recognition systems primarily rely on supervised learning techniques to classify and recognize faces based on labeled datasets. However, reinforcement learning (RL), a machine learning paradigm focused on training models through interactions and feedback from the environment, presents a new approach to enhance the adaptability and performance of facial recognition systems. This paper explores the implementation of facial recognition using reinforcement learning, focusing on the advantages RL offers in terms of continuous learning and real-time adaptation. By utilizing an RL agent to improve the feature extraction and classification process, the proposed method dynamically adapts to changing environmental conditions and new facial data, providing more robust recognition capabilities. This paper provides a comprehensive discussion of the proposed model, its architecture, and experimental results.