MATLAB Implementation of Face Recognition System

In this face recognition program, the process begins with comprehensive image preprocessing. This involves several key operations: image cropping to focus on facial regions, grayscale conversion to reduce color complexity, and histogram equalization to enhance contrast and normalize illumination conditions. These preprocessing steps are crucial for optimizing feature extraction performance. The core algorithm utilizes Principal Component Analysis (PCA) for feature extraction. This technique works by computing eigenvectors from the covariance matrix of training images, effectively reducing dimensionality while preserving the most significant facial features. The PCA implementation typically involves calculating mean-centered images, constructing a covariance matrix, and selecting principal components corresponding to the largest eigenvalues. This dimensionality reduction significantly decreases computational complexity and improves algorithm execution speed. Following feature extraction, the program employs a classifier (commonly k-Nearest Neighbors or Support Vector Machines) to categorize input images. The classification phase compares extracted features against a trained database to determine whether the image contains the target face. The entire system demonstrates robust performance through sophisticated algorithmic processing, achieving high-precision face recognition that provides strong support for various practical applications such as security systems and biometric authentication.