ML-Driven Palm Print Authentication System for Security Applications

Abstract

Biometric authentication systems have emerged as robust and reliable methods for security. Among various biometric traits, palm prints are gaining increasing attention due to their unique characteristics, ease of acquisition, and resilience to forgery. This paper
proposes an ML-driven palm print authentication system that leverages machine learning algorithms to enhance security applications. The system combines feature extraction, pre-processing, and classification techniques to achieve high accuracy and robustness in authentication. The proposed model is evaluated using publicly available datasets, and results demonstrate its efficacy in terms of accuracy, speed, and adaptability.