Face Recognition Using PCA (Principal Component Analysis) with MATLAB Implementation

This MATLAB program implements face recognition using PCA (Principal Component Analysis), employing sophisticated mathematical formulas and algorithms to ensure high success rates. The core implementation involves matrix decomposition techniques from linear algebra and statistical principles from probability theory. Key computational steps include: covariance matrix calculation from normalized face datasets, eigenvalue decomposition to extract principal components, and projection of test images onto the reduced-dimension feature space. The program incorporates comprehensive image preprocessing operations such as noise removal using Gaussian filters, contrast enhancement through histogram equalization, and color space normalization. Through these advanced technical approaches, our program achieves highly accurate face recognition with robust performance across varying environmental conditions. Additionally, we have integrated user-friendly features including real-time image preview capabilities and adjustable threshold parameters for similarity matching (e.g., Euclidean distance thresholds), enabling intuitive operation and customization for end-users.