Gabor Wavelet Transform and PCA for Face Recognition

The face recognition method combining Gabor wavelet transform with PCA is a classic biometric technique suitable for graduation projects or introductory research. Its core principle involves extracting facial texture features using multi-scale, multi-directional Gabor filters, followed by dimensionality reduction and key feature extraction through PCA, ultimately achieving recognition through classifiers. The advantage of Gabor wavelet transform lies in its ability to simulate human visual system sensitivity to textures. Through filter banks with different frequencies and orientations, it captures local facial features such as edges and wrinkles. This transformation generates high-dimensional feature vectors that contain rich information but also introduce computational complexity and redundancy issues. PCA (Principal Component Analysis) serves to reduce the dimensionality of Gabor features while retaining the most discriminative components. By calculating eigenvectors of the covariance matrix, it projects data into a lower-dimensional space, reducing computational load while improving classification efficiency. In practical implementation, the typical workflow includes: image preprocessing (e.g., grayscale conversion, normalization), application of Gabor filter banks to extract magnitude or phase features, PCA for feature dimension compression, and finally using classifiers like SVM or KNN for training and prediction. This pipeline balances feature representation capability with computational efficiency, making it suitable for academic research and medium-to-small scale datasets. For graduation projects or beginners, this method covers fundamental image processing techniques (filtering, transformation) while introducing core machine learning concepts (feature dimensionality reduction, classification). It serves as an excellent case study for understanding traditional face recognition frameworks, with implementation typically involving OpenCV for image processing and scikit-learn for PCA and classifier components.