MATLAB Implementation of Palmprint Recognition with Feature Extraction and Matching Algorithms

In this article, we present a comprehensive MATLAB implementation for palmprint recognition and discuss the key methodologies involved. The system incorporates several critical algorithms: maximum inscribed circle detection for accurate region-of-interest (ROI) localization, KL transformation (also known as Principal Component Analysis) for efficient feature dimensionality reduction, wavelet transform for multi-scale texture analysis, and Euclidean distance measurement for robust pattern matching. Palmprint recognition is a biometric authentication technology that analyzes and compares the unique ridge patterns on human palms. This technology has extensive applications in security systems and biometric identification domains. The MATLAB implementation typically follows these processing stages: First, the maximum inscribed circle algorithm detects the palm's central region using morphological operations and boundary tracing to eliminate background interference. Then, KL transformation (implemented through eigendecomposition of covariance matrices) extracts dominant features while reducing computational complexity. Wavelet transform (using functions like wavedec2) decomposes the palmprint image into multiple frequency bands to capture both global and local texture characteristics. Finally, Euclidean distance calculation (norm function in MATLAB) compares feature vectors between query and database samples for classification. By integrating these methods, we significantly enhance the accuracy and reliability of palmprint recognition systems, making them suitable for practical applications such as access control systems, payment verification, and personal identification solutions. The code structure employs MATLAB's image processing toolbox functions alongside custom implementations for optimal performance in feature extraction and matching operations.