Enhancing ATM Security Through Biometric Face Recognition

Facial recognition systems are sophisticated computer applications designed to automatically identify or verify individuals using digital images or video frames. This paper proposes the integration of facial recognition technology as an authentication mechanism in Automated Teller Machines (ATMs). Facial recognition operates through two key processes: verification, which determines whether an individual matches the claimed identity, and identification, which ranks the individual against a database of faces. By leveraging convolutional neural networks (CNNs), this technology analyzes the unique structure, patterns, and positioning of facial features to ensure robust security. The reliance on traditional ATM authentication methods such as physical cards and PIN codes has notable drawbacks. Users may misplace cards, forget PINs, or encounter damage to the cards, rendering access to funds challenging. To address these limitations, this study emphasizes biometrics, particularly facial recognition, as a superior alternative. By replacing cards and PINs with facial verification, the risk of unauthorized access by illegitimate users possessing valid credentials is minimized. Authentication is achieved by comparing the image captured at the ATM to those stored in a secure database, ensuring enhanced security and user convenience. This approach underscores the potential of biometrics in revolutionizing ATM systems for better accessibility and fraud prevention.