Face Recognition Using MATLAB with PCA Implementation

This documentation presents a face recognition system implemented using Principal Component Analysis (PCA) methodology to differentiate between human faces and non-face images. This technique holds significant importance, particularly for major assignments in stochastic processes courses. PCA is a mathematical technique rooted in linear algebra that transforms original data into a new set of uncorrelated variables called principal components. These components capture the majority of variance present in the original dataset. Through PCA implementation in MATLAB, we can enhance processing efficiency and accuracy when handling large-scale image data. The algorithm typically involves key steps: data normalization, covariance matrix computation, eigenvalue decomposition, and principal component selection based on variance thresholds. In the realm of face recognition, PCA has gained widespread adoption due to its ability to reduce data dimensionality while preserving critical facial features. The MATLAB implementation generally includes functions for image preprocessing, eigenface calculation, and classification using distance metrics like Euclidean or Mahalanobis distance. For the stochastic processes course project, applying PCA-based face recognition enables deeper understanding of this fundamental technique and its practical application in solving real-world pattern recognition problems. The code structure typically involves matrix operations for eigenvector computation and projection of test images onto the reduced-dimensional space for classification.