Face Recognition Using PCA Method with Source Code Implementation

In undergraduate graduation projects, face recognition using the PCA (Principal Component Analysis) method represents a significant research topic with practical applications. This approach can be implemented in various domains including facial recognition access control systems, security surveillance, and biometric payment authentication. The PCA algorithm operates by reducing the dimensionality of face images while preserving essential features through eigenvalue decomposition of the covariance matrix. Key implementation steps include: image preprocessing and normalization, covariance matrix calculation, eigenvalue extraction, and projection onto the principal component space. The recognition process involves comparing test images with trained templates using distance metrics like Euclidean distance. This paper provides complete source code implementation (typically in MATLAB or Python) that demonstrates the entire workflow from image loading and preprocessing to classification. The code includes functions for: data matrix construction, mean face calculation, covariance computation, and feature vector projection. Experimental images are also provided to validate the algorithm's performance under different conditions, showing practical examples of face detection accuracy and computational efficiency. We believe this research offers substantial reference value for individuals interested in face recognition, pattern recognition, and computer vision applications, serving as both an educational resource and practical implementation guide.