Face Recognition Method Based on Gabor Wavelet Transform and Artificial Neural Network with MATLAB Implementation

This article presents a face recognition methodology utilizing Gabor wavelet transform and artificial neural networks, accompanied by comprehensive MATLAB code. The approach synergistically combines the robust feature extraction capabilities of Gabor wavelet transforms with the powerful pattern recognition abilities of artificial neural networks to achieve effective face identification. The implementation involves processing facial images through Gabor wavelet transformation to extract critical texture features, which are then fed as input vectors to the neural network for classification and recognition tasks. The Gabor filter bank implementation typically involves multiple orientations and scales to capture facial characteristics comprehensively, while the neural network architecture employs supervised learning for accurate pattern matching. This methodology demonstrates significant potential in facial recognition applications, including security systems, access control, and biometric authentication platforms. The provided MATLAB code includes key functions for Gabor feature extraction, neural network training using backpropagation algorithms, and recognition accuracy evaluation. Readers can leverage this implementation to gain practical understanding of feature extraction parameters, network configuration settings, and performance optimization techniques, thereby enhancing their expertise in computer vision and pattern recognition domains. The code structure encompasses image preprocessing routines, Gabor filter implementation with customizable parameters, feature vector normalization, neural network initialization with configurable layers, and comprehensive testing modules for validation. This hands-on approach enables researchers and practitioners to experiment with different network architectures and Gabor parameters to optimize recognition performance for specific applications.