Gabor Feature Based Classification Using 2D Linear Discriminant Analysis for Face Recognition

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Abstract

This paper introduces a novel 2D Gabor-Fisher Classifier for face recognition. The 2D-GFC method applies the 2D Fisher Linear Discriminant Analysis (2D-LDA) to the gaborfaces which is derived from the Gabor wavelets representation of face images. In our method, Gabor wavelets first derive desirable facial features characterized by spatial frequency, spatial locality, and orientation selectivity to cope with the variations due to illumination and facial expression changes. 2D-LDA is then used to enhance the face recognition performance by maximizing the Fisher’s linear projection criterion. To evaluate the performance of 2D-GFC, experiments were conducted on FERET database with several other methods.