Gabor 2DPCA Face Recognition Algorithm

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Resource Overview

A custom-implemented Gabor 2DPCA face recognition algorithm that extracts Gabor features and performs recognition using 2DPCA. Tested on the Yale face database with high accuracy and fast processing speed. The code allows direct recognition rate output by simply adjusting the number of training samples. Includes pre-loaded Yale database for immediate execution and result visualization - implements Gabor filter convolution, 2DPCA dimensionality reduction, and classification modules.

Detailed Documentation

This article presents my implementation of the Gabor 2DPCA face recognition algorithm. The algorithm utilizes Gabor feature extraction followed by 2DPCA for facial recognition. Key implementation aspects include: applying multi-orientation Gabor filters to capture texture features, using 2DPCA for efficient dimensionality reduction while preserving image matrix structure, and employing nearest neighbor classification. Testing on the Yale database demonstrated high recognition accuracy and computational efficiency. The modular code design allows users to directly modify the training sample size parameter to obtain recognition rates. The package includes the integrated Yale face database, enabling immediate execution with output displaying recognition results and performance metrics. The algorithm represents a robust, fast, and accurate face recognition solution with substantial practical application potential.

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