MATLAB Source Code Implementation for Face Recognition System

This section introduces the implementation approach of a MATLAB-based face recognition system, which primarily employs Principal Component Analysis (PCA) and Nearest Neighbor Distance classification algorithm.

### System Overview A typical face recognition system involves three critical stages: preprocessing, feature extraction, and classification recognition. MATLAB's Image Processing Toolbox and matrix computation capabilities enable efficient implementation of these stages.

#### 1. Preprocessing The preprocessing phase mainly includes image normalization, grayscale conversion, and size standardization. Input face images may vary due to lighting conditions, angles, or dimensions, requiring standardization to a fixed size (e.g., 100×100 pixels) and conversion to grayscale to reduce computational complexity. Code Implementation: Use imresize() for dimensional standardization and rgb2gray() for color conversion, followed by histogram equalization for illumination normalization.

#### 2. Feature Extraction (PCA) Principal Component Analysis (PCA) serves as the core algorithm for dimensionality reduction and feature extraction. Implementation workflow: Training Matrix Construction: Flatten all training images into column vectors and combine them into a large matrix. Mean Face Calculation: Compute the average face from all training images and subtract it to center the data. Covariance Matrix and Eigenvectors: Obtain principal components (eigenfaces) through Singular Value Decomposition (SVD) or eigenvalue decomposition of the covariance matrix. Select top-k eigenvectors corresponding to the largest eigenvalues as the projection subspace. Dimensionality Reduction Projection: Project original images onto the eigenface space to obtain low-dimensional feature vectors (typically much smaller than original pixel dimensions). MATLAB Functions: Utilize svd() or eig() for matrix decomposition, with careful attention to memory optimization for large datasets.

#### 3. Classification Recognition (Nearest Distance) After extracting test image feature vectors, apply nearest distance classification (e.g., Euclidean distance): Calculate distances between the test feature vector and all training feature vectors. Assign the class label of the training sample with minimum distance as the recognition result. Optional Enhancement: Implement K-Nearest Neighbors (KNN) voting mechanism to improve robustness against outliers. Code Implementation: Use pdist2() for efficient distance computation and mode() function for KNN voting when implemented.

### Enhancement Strategies Performance Optimization: Replace PCA with Local Binary Patterns (LBP) or deep learning models (e.g., CNN) to improve recognition rates in complex scenarios. Real-time Improvement: Accelerate PCA projection using MATLAB's Parallel Computing Toolbox for multicore processing. Application Scenarios: The system can be extended to practical applications like access control and attendance systems, requiring integration with liveness detection for security enhancement.

This implementation demonstrates MATLAB's advantages in matrix operations and algorithm prototyping, serving as an excellent introductory case for understanding classical face recognition pipelines.