Gabor Wavelet Feature Extraction for Palmprint, Face, and Fingerprint Recognition

This approach utilizes Gabor wavelets for feature extraction followed by Support Vector Machine (SVM) classification, applicable to palmprint, face, and fingerprint recognition systems. Gabor wavelets effectively capture texture information from images through multi-scale and multi-orientation filtering, while SVM provides robust classification for the extracted features. Implementation typically involves applying Gabor filters at multiple frequencies and orientations to generate feature vectors, which are then normalized before SVM classification. Key functions include Gabor filter bank generation, convolution operations for feature extraction, and SVM training with kernel functions like RBF for optimal separation. This combined methodology has broad applications in biometric recognition, including security authentication, personal identity verification, and access control systems. The Gabor-SVM combination demonstrates significant potential in image processing and biometric recognition due to its ability to handle complex texture patterns and provide high classification accuracy.