A Biometric Recognition-Based Authentication System: Development of a Secure Prototype Focusing on Facial Recognition

Abstract

Traditional password-based authentication is increasingly inadequate to protect online services, as it remains vulnerable to brute-force attacks, credential theft, and social engineering tactics. These threats are further exacerbated by common user behaviors, namely password reuse across different platforms and weak password selection. In response, facial recognition has emerged as a promising alternative, offering an intuitive and user-friendly form of authentication. However, existing facial recognition systems present their own challenges, including vulnerability to spoofing, privacy concerns, and inconsistent performance in different environments. This thesis presents the design and implementation of an authentication system, with a primary focus on a facial recognition system developed to address these limitations. A comparative analysis of available models identified YuNet as the most effective model for face detection, and FaceNet for face recognition. The system architecture incorporates a Flask-based backend with a responsive and user-friendly frontend interface, enabling secure user registration and login. To enhance accessibility, multiple alternative login methods have been incorporated, allowing users without camera access to authenticate securely. Additionally, to strengthen data security and privacy, all sensitive user information is securely encrypted before being stored. The system’s performance was evaluated using standard classification metrics and carefully optimized to achieve an effective balance between security and usability. The result is an authentication framework that addresses both user convenience, modern security demands, and ethical concerns.