Gabor feature based class-dependence feature analysis for face recognition

Zhongkai Han; Chi Fang; Xiaoqing Ding

doi:10.1117/12.840070

8 February 2010 Gabor feature based class-dependence feature analysis for face recognition

Zhongkai Han, Chi Fang, Xiaoqing Ding

Proceedings Volume 7532, Image Processing: Algorithms and Systems VIII; 75320M (2010) https://doi.org/10.1117/12.840070
Event: IS&T/SPIE Electronic Imaging, 2010, San Jose, California, United States

Abstract

In this paper, we introduce a novel Gabor based Spacial Domain Class-Dependence Feature Analysis(GSD-CFA) method that increases the Face Recognition Grand Challenge (FRGC)2.0 performance. In short, we integrate Gabor image representation and spacial domain Class-Dependence Feature Analysis(CFA) method to perform fast and robust face recognition. In this paper, we mainly concentrate on the performances of subspace-based methods using Gabor feature. As all the experiments in this study is based on large scale face recognition problems, such as FRGC, we do not compare the algorithms addressing small sample number problem. We study the generalization ability of GSD-CFA on THFaceID data set. As FRGC2.0 Experiment #4 is a benchmark test for face recognition algorithms, we compare the performance of GSD-CFA with other famous subspace-based algorithms in this test.

Citation Download Citation

Zhongkai Han, Chi Fang, and Xiaoqing Ding "Gabor feature based class-dependence feature analysis for face recognition", Proc. SPIE 7532, Image Processing: Algorithms and Systems VIII, 75320M (8 February 2010); https://doi.org/10.1117/12.840070

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