Gabor+PCA Face Recognition with MATLAB Implementation

This MATLAB implementation of face recognition using Gabor filters combined with Principal Component Analysis (PCA) achieves exceptionally high recognition rates. The method effectively integrates Gabor filter technology for texture feature extraction with PCA for dimensionality reduction and feature selection. The Gabor filter bank processes facial images to capture multi-scale and multi-orientation texture information, while PCA reduces the feature dimensionality by selecting the most discriminative components. The code typically involves creating Gabor filter banks with multiple frequencies and orientations, applying them to facial images to generate feature vectors, and then using PCA to project these high-dimensional features into a lower-dimensional subspace. This combined approach enables more accurate and reliable face recognition results by preserving the most significant facial characteristics while eliminating redundant information. Consequently, the Gabor+PCA face recognition algorithm proves to be a highly effective and robust method suitable for various applications including security systems, face unlock mechanisms, and facial detection technologies.