Classical PCA Face Recognition Method and PCA+SVM Face Recognition Approach

Classical PCA Face Recognition Method

PCA (Principal Component Analysis) is a classical unsupervised dimensionality reduction algorithm widely applied in face recognition. Its core principle involves transforming high-dimensional face images into low-dimensional feature space through orthogonal transformation, preserving principal components with maximum variance directions as features. The traditional implementation workflow includes:

Flattening training set face images into column vectors to construct sample matrix Computing mean face and performing centralization processing Solving eigenvalues and eigenvectors of covariance matrix Selecting top k eigenvectors corresponding to largest eigenvalues to form projection matrix Projecting new samples through projection matrix to obtain low-dimensional feature vectors

The recognition phase employs nearest neighbor classifier, matching test sample features with training set features using Euclidean distance calculation. This method demonstrates computational efficiency but is limited by linear assumptions and sensitive to illumination and angle variations.

PCA+SVM Enhanced Approach

The improved scheme combining Support Vector Machine (SVM) effectively enhances recognition rates:

Front-end still utilizes PCA for feature extraction but retains more principal components (e.g., dimensions covering 95% energy proportion) Using dimension-reduced features as input vectors for SVM Selecting RBF kernel function to handle non-linearly separable cases Optimizing SVM's penalty parameter C and kernel parameter γ through cross-validation

This hybrid model's advantage lies in: after PCA eliminates data redundancy, SVM can better construct classification hyperplanes. Particularly when training samples are limited, SVM's structural risk minimization principle prevents overfitting. Experiments show that on standard datasets like ORL, PCA+SVM can improve recognition rates by 8-15% compared to pure PCA methods.

Important considerations: Both methods require face alignment and grayscale normalization preprocessing. When handling scenarios with significant occlusion or expression variations, combining with local feature methods like LBP is recommended.