Face Recognition Using ICA (Independent Component Analysis) Method

In this project, we present a face recognition program based on the ICA (Independent Component Analysis) method. We provide a comprehensive face database that users can easily download and utilize for experimentation. Our implementation employs the FastICA algorithm to extract statistically independent components from facial images, enhancing feature discrimination capabilities. The program includes preprocessing stages for image normalization and dimensionality reduction using principal component analysis (PCA) as an optional preliminary step. This tool is designed to serve as an accessible learning resource for individuals interested in understanding face recognition techniques. The code structure demonstrates practical implementation of signal separation algorithms, featuring modules for training data organization, component transformation, and classification based on cosine similarity matching. We encourage users with interest in biometric recognition to experiment with our program. For any technical inquiries or improvement suggestions, please feel free to contact our development team.