Kernel-Plural Discriminant Analysis Based on Fourier Transform and Its Application to Face Recognition

Fourier transform is a widely used image processing technology. Kernel discriminant analysis is an effective nonlinear feature extraction technique. Based on them, we propose a novel feature extraction approach for face recognition. First, we perform the Fourier transform on face images and express the Fourier frequency bands in the plural form. By computing the kernel-plural discriminant capability of every frequency band, we choose the bands with strong capabilities and use them to form a new sample set. Then, we extract nonlinear discriminant features from the set and classify it by using the nearest neighbor classifier. Experimental results on AR and Feret face databases demonstrate the effectiveness of the proposed approach.