MATLAB Implementation of Face Recognition with Gabor Filtering and Neural Network Training

A face recognition program is a computer application that utilizes Gabor filtering and neural network training to identify human faces. Face recognition represents a crucial technology with diverse applications across multiple domains. In security systems, it enables identity verification and access control mechanisms. For social media platforms and photo management applications, face recognition facilitates automated photo tagging and categorization. Additionally, this technology finds applications in human-computer interaction, such as facial unlock features for mobile devices and computers. The implementation typically involves two key computational stages: First, Gabor filtering extracts multi-scale and multi-orientation facial features by applying a bank of Gabor filters that simulate human visual cortex responses. This process generates enhanced feature representations capturing texture patterns and edge information. Second, neural network training (often using MATLAB's Neural Network Toolbox) learns discriminative patterns from these features through supervised learning algorithms like backpropagation. The network architecture may include multiple hidden layers for deep feature learning, with activation functions like ReLU optimizing gradient flow during training. Common implementation involves preprocessing steps (face detection, normalization), feature extraction using 2D Gabor wavelets, and classification through trained neural networks, significantly improving recognition accuracy and system performance.