Face Recognition based on a Gabor-2D Fisherface Approach with Selecting 2D Gabor Principal Components and Discriminant Vectors

摘要

In this paper, a novel Gabor-2DFisherface approach with selecting 2D Gabor principal components and discriminant vectors is proposed for face recognition. Gabor transform is an important frequency-domain analysis tool. The proposed approach combines it with discriminant analysis technique. This approach first preprocesses all image samples by using Gabor transform, and then calculates 2D Gabor principal components and discriminant vectors by using 2DFisherface method. To enhance the discriminant capability, an automatic strategy is employed to select these components and vectors. After extracting the discriminant features, this approach adopts the nearest neighbor classifier with cosine distance for classification. The experimental results on the public AR face database demonstrate that the proposed approach outperforms several related discrimination methods.