Paper Title
Human Identification Based on Hand Biometric Modality Using Machine Learning

Abstract
This paper is centered on creating a biometric identification system that relies on the unique physical Characters of a person’s hand to ensure secure and accurate and verification of their identity. This uses advanced Machine Learning techniques like Random Forest, KNN to examine unique hand characteristics, thus permitting accurate Recognition that does not require unique methods of passwords, cards, or keys. The system design is done with robustness in mind and ensures that it works well across different conditions and environments. It addresses the growing need for modern, efficient identification solutions by offering a seamless user experience and enhancing overall security. This approach minimizes vulnerabilities associated with conventional identification methods, such as the risk of theft or duplication. The proposed method is aversatile, including scenarios suchas secure access control for restricted areas, feature extractions Methods like PCA and LDA have been used for analyzing streamlined identity verification incritical sectorslike banking or healthcare, and other situations where robust authentication is essential. by focusing on a biometric-driven solution, this approach aims to provide a practical and innovative tool to meet the demands of an increasingly security-conscious world. Keywords - Cyber security, Biometric Identification System, Non-traditional Authentication, Risk Minimization, Authentication Innovation, Enhanced Security, Principal component analysis, Real-time analysis, Machine learning optimization.