Block Statistics Based Gabor Feature Representation and Its Application to Face Recognition
LONG Fei1,2, YE XueYi1, LI Bin1, YAO Peng1, ZHUANG ZhenQuan1
1.Department of Electronic Science and Technology, University of Science and Technology of China, Hefei 230026 2.Software School, Xiamen University, Xiamen 361005
Abstract Face representation based on Gabor features has attracted much attention and achieved great success in face recognition for some favorable attributes of Gabor wavelets such as spatial locality and orientation selectivity. A large number of Gabor features are produced with varying parameters in the position, scale and orientation of filters. In some existing methods, useful discriminatory information may be lost due to downsampling Gabor features directly. To reduce the loss, a block statistics based Gabor feature representation method is proposed. The effectiveness of this method is demonstrated by template matching test on ORL face database, and the comparative test results show that this method can yield better recognition accuracy with much fewer Gabor features as well as less CPU time of feature matching than the existing approach of downsampling based Gabor feature representation. In addition, Generalized Discriminant Analysis (GDA) which performs dimensionality reduction to Gabor features is used to produce more compact and discriminatory face representation. The experimental results of face recognition using different similarity measures show that the proposed method outperforms the famous Eigenfaces and Fisherfaces methods significantly, and the rationality of this combination is also comparatively demonstrated.
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2006, Vol. 19 
