Application of Gabor Wavelets in Face Recognition with MATLAB Implementation

In the field of face recognition, Gabor wavelets serve as a powerful tool for extracting discriminative facial features. The MATLAB implementation typically involves creating a bank of Gabor filters with different orientations and scales to capture texture information at multiple frequencies. Key functions include gaborFilterBank() for generating filter parameters and applyGaborFilters() for convolving filters with facial images to extract Gabor features. This approach can be effectively implemented in graduation projects through MATLAB programming, where students can utilize built-in functions like imgaborfilt() for efficient filter application. The process generally involves preprocessing facial images, applying Gabor filters at multiple orientations (typically 4-8 directions) and scales (3-5 frequencies), and then extracting magnitude responses to form feature vectors. Additionally, numerous high-quality resources related to Gabor wavelets are available for deeper understanding of this domain. These resources include technical papers discussing optimal parameter selection, research reports on feature extraction optimization, and professional books covering mathematical foundations. By studying these materials, researchers can expand their knowledge and obtain substantial references and support for their graduation projects, particularly in areas like feature dimensionality reduction using PCA or LDA and classification with SVM or neural networks.