Gabor Wavelet Transform and PCA Face Recognition

In my graduation project, I developed a comprehensive implementation of Gabor wavelet transform combined with Principal Component Analysis (PCA) for face recognition. The system extracts facial features using Gabor filters at multiple orientations and scales, followed by PCA for dimensionality reduction to enhance recognition efficiency. The code implements key functions including Gabor filter bank generation, feature vector extraction, covariance matrix computation, and eigenvalue decomposition for optimal feature selection. This project demonstrates practical integration of multi-resolution analysis with statistical pattern recognition techniques. I believe this implementation can be particularly helpful for those who, like me once were, seeking clarity in combining signal processing with machine learning approaches for biometric applications. Through my well-documented code and technical explanations, I aim to help others better understand both the theoretical foundations and practical implementation aspects. My graduation project serves as a reference implementation that showcases how Gabor features' orientation selectivity complements PCA's dimensionality reduction capabilities. I'm also keen to share my experiences and insights regarding algorithm optimization, parameter tuning, and performance evaluation metrics with anyone interested in this project.