BioLogic: Neural-Powered Palm Print Authentication for Next-Gen Security

A secure biometric palm authentication system provides a systematic and logical method for identifying individuals using the unique ridges visible on their palms. Palm prints offer a more reliable and safer alternative to conventional verification methods such as passwords or PIN numbers, which can be easily forgotten, stolen, or shared. The principal lines and creases of palm prints are rich global features essential for precision authentication. This study explores the development of a logical authentication system using palm print evaluation strategy based on neural classification logic. The proposed system leverages the unique and complex patterns of palm prints to achieve high accuracy in identity verification. Utilizing a Hybrid Neural Network Model (HNNM) combining Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Support Vector Machines (SVMs), the system demonstrates superior performance metrics. The model achieves an accuracy of 97.5%, precision of 97.2%, and recall of 97.3%, indicating its robustness and reliability.