MATLAB Code Implementation for Palmprint Recognition

This is a palmprint recognition program implemented in MATLAB that employs Gabor algorithm for feature extraction and Support Vector Machine (SVM) for classification. Palmprint recognition is a biometric identification technology that analyzes and compares the ridge patterns and morphological characteristics of human palms for authentication purposes. The program's design focuses on creating efficient, accurate, and secure identity verification and access control systems. The implementation uses Gabor filters to extract distinctive features from raw palmprint images through multi-scale and multi-orientation filtering operations. These filters capture both spatial frequency and orientation information, producing feature vectors that effectively differentiate between individuals. The code typically involves creating a bank of Gabor filters with varying frequencies and orientations, followed by convolution operations with the input image to generate the feature set. The Support Vector Machine classifier then processes these extracted features using kernel functions to create optimal decision boundaries in high-dimensional space. The MATLAB implementation likely utilizes functions from the Statistics and Machine Learning Toolbox, such as fitcsvm for model training and predict for classification. The SVM component handles the pattern recognition task by mapping features to distinct classes, enabling accurate matching and identification of palmprint images. Palmprint recognition technology holds significant application potential in security systems and personal authentication, suitable for access control systems, mobile device unlocking, banking transactions, and various other scenarios that require convenient and reliable identity verification methods.