Face Detection and Recognition using Eigenface Method

This project implements face detection and recognition using the Eigenface method through MATLAB programming. The Eigenface approach is a classical facial recognition technique that analyzes facial features by applying Principal Component Analysis (PCA) to extract the most significant patterns from face images. In our MATLAB implementation, we process facial images by converting them to grayscale, normalizing pixel values, and computing the covariance matrix to derive eigenfaces. The core algorithm involves calculating eigenvalues and eigenvectors from the training dataset, then projecting new face images onto the eigenface space for comparison using distance metrics like Euclidean distance. Key MATLAB functions utilized include pca() for dimensionality reduction, imread() for image processing, and various matrix operations for efficient computation. This implementation ensures reliable face detection and recognition while maintaining computational efficiency, providing a practical demonstration of how Eigenface methodology can be applied in real-world scenarios. The project serves as an educational resource for understanding and applying Eigenface-based techniques in computer vision applications.