Face Recognition Algorithm Mini-Experiment in MATLAB Environment

In the MATLAB environment, I developed a mini-experiment for face recognition algorithms utilizing the classical Principal Component Analysis (PCA) method. The experiment's primary implementation involves using Singular Value Decomposition (SVD) to extract PCA principal component features from pixel space, where the SVD function (svd()) efficiently computes eigenvectors and eigenvalues from the covariance matrix of face images. These extracted features provide significant insights into facial image characteristics and structural patterns by reducing dimensionality while preserving critical visual information. For the classification component, the implementation employs a straightforward minimum distance classification algorithm, where unknown faces are classified by calculating Euclidean distances between their feature vectors and stored template vectors, then assigning them to the class with the smallest distance. This experimental work demonstrates practical understanding of PCA fundamentals through hands-on coding, particularly in optimizing algorithm parameters like the number of principal components and distance thresholds for improved recognition accuracy. Through result analysis, the experiment effectively illustrates PCA's applications in face recognition systems, highlighting its advantages in dimensionality reduction and computational efficiency, while also addressing limitations such as sensitivity to lighting conditions and facial expressions. The code implementation includes key functions for data preprocessing, covariance matrix computation, eigenvalue decomposition, and distance-based classification, providing a comprehensive foundation for understanding pattern recognition principles.