Palmprint Recognition-Based Online Identity Verification Algorithm

This paper presents an online identity verification algorithm based on palmprint recognition, designed for personal authentication systems. The algorithm utilizes palmprint features as biometric identifiers, capturing users' palmprint data through specialized acquisition devices. Key implementation aspects include palmprint image preprocessing using OpenCV libraries, feature extraction through Gabor filter banks and local binary patterns (LBP), and matching algorithms employing Euclidean distance classifiers. During the undergraduate thesis development, we established a comprehensive workflow involving palmprint data collection from multiple subjects, image enhancement techniques for noise reduction, and performance evaluation metrics including False Acceptance Rate (FAR) and False Rejection Rate (FRR). Experimental results demonstrated the algorithm's effectiveness with approximately 94.3% recognition accuracy under controlled lighting conditions. The complete implementation code in Python with detailed documentation is available for academic reference, providing valuable insights for students working on similar biometric authentication projects.