Face Recognition for 100 Subjects Using SOM Neural Network Algorithm

This project implements face recognition for 100 subjects using the Self-Organizing Map neural network algorithm in MATLAB. The algorithm demonstrates excellent scalability through its modular architecture. The SOM neural network effectively classifies and identifies facial images by learning topological representations of input patterns. During training, the algorithm automatically adapts its weights to distinguish different facial features through competitive learning and neighborhood function adjustments. Key implementation aspects include: - Preprocessing of facial images using dimensionality reduction techniques - SOM network initialization with proper grid dimensions and learning parameters - Iterative weight updates through winner-takes-all competition mechanism - Neighborhood function implementation for topological preservation The method finds extensive applications in image processing and pattern recognition domains, achieving remarkable results in face recognition technology. By employing SOM neural networks, we can better analyze various facial features embedded in image data, thereby enhancing recognition accuracy and reliability. The algorithm's unsupervised learning capability allows it to discover inherent patterns in facial data without labeled training sets, making it particularly suitable for large-scale face recognition systems.