Gabor Wavelet Feature Extraction for Biometric Recognition: Palmprint, Face, and Fingerprint Applications

Gabor wavelets represent a widely used image feature extraction technique that transforms images into feature vectors containing rich spatial and frequency domain information. The implementation typically involves applying multiple Gabor filters with different orientations and scales to capture texture patterns effectively. Support Vector Machine (SVM) serves as a robust classifier that performs classification and recognition based on input feature vectors. By integrating Gabor wavelet features with SVM classifiers, we can develop effective applications in biometric recognition domains including palmprint, face, and fingerprint identification. Key implementation steps include: preprocessing input images, computing Gabor filter responses across multiple orientations (typically 0°, 45°, 90°, 135°), extracting magnitude features from filter responses, and training SVM classifiers with radial basis function (RBF) kernels. Through extracting and analyzing Gabor wavelet features from different images, we achieve more accurate and reliable recognition results, thereby enhancing the performance and credibility of recognition systems. The feature extraction process can be optimized using parallel computation techniques for real-time applications.