Exploring the realm of biometric authentication, this study delves into dorsal hand vein patterns as a distinctive and secure means of identity verification. Employing non-intrusive near-infrared sensors, dorsal hand vein images undergo sophisticated processing using advanced machine learning algorithms, including Convolutional Neural Networks (CNNs). The resulting authentication system undergoes rigorous training, validation, and evaluation using key performance metrics. Comparative analyses underscore the unique advantages of dorsal hand vein authentication, while addressing practical considerations such as user acceptability and ethical implications. This non-intrusive and forgery-resistant biometric method contributes significantly to the field of secure identification, finding potential applications in finance, healthcare, and secure facility access.
