Face Detection and Recognition Algorithm with PCA Implementation

The face detection and recognition algorithm developed in MATLAB environment employs Principal Component Analysis (PCA) for the recognition module, which serves as a classic linear dimensionality reduction method to efficiently extract essential features from facial images. The PCA implementation typically involves calculating eigenvalues and eigenvectors from the covariance matrix of training images, creating an eigenface space for efficient pattern matching. The system includes a comprehensive graphical user interface (GUI) that enables users to intuitively operate and execute the algorithm without requiring command-line expertise. The main entry point is the facedec program (likely facedec.m), which initializes the GUI components and loads the necessary PCA model data. When executed, this function handles image preprocessing, feature extraction using PCA projection, and classification through distance measurements in the reduced feature space. To utilize this robust face recognition system, simply run the facedec program which orchestrates the entire pipeline from face detection to final identification, providing real-time feedback through the interactive interface. The algorithm automatically handles image normalization and compares input faces against the trained dataset using Euclidean distance or similar metrics in the PCA-transformed space.