AI-Driven Palm Print Authentication: A comprehensive Analysis of Deep Learning Approaches for Efficient Biometrics

Palmprint recognition is a kind of biometric identification that confirms a person's identity by analyzing certain discriminative characteristics found on their palm. The benefits of contactlessness, stability, and security have made it a popular choice. This is why a palmprint is a reliable biometric for human identification due to its unique characteristics. The area of people recognition is only one of several that has benefited from the proliferation of better computer techniques and uses of artificial intelligence (AI). In this research, we survey the state-of-the-art in image processing as it pertains to palm print identification and authenticity, and compare and contrast several approaches. This work focuses on a palm print feature that has garnered a lot of interest in the literature recently in this study. This work developed a deep learning model focusing on the palm print verification challenge. Not only that, but the study details the steps taken by biometric authentication systems, including preprocessing, feature enhancement, feature extraction, the classification process of palm print pattern recognition systems, and a number of experimental results pertaining to palm print pattern recognition methods. The CNN model outperforms competing algorithms in palm print recognition, with an accuracy of 98.5%. Various algorithmic and palm print identification approaches have been developed and successfully implemented in continuous evaluation. In this study, examine all of the strategies and formulate a superior one based on this survey. Each method requires different previous information. Additionally, a new method for contactless palm print identification and authentication was suggested in this study.