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Discriminant analysis with Gabor phase feature for robust face recognition

[+] Author Affiliations
Hong Han

Xidian University, School of Electronic Engineering, Mailbox 224, No. 2 Taibai South Road, Xi’an 710071, China

Jianfei Zhu

Xidian University, School of Electronic Engineering, Mailbox 224, No. 2 Taibai South Road, Xi’an 710071, China

Zhen Lei

Chinese Academy of Sciences, Center for Biometrics and Security Research & National Laboratory of Pattern Recognition, Institute of Automation, No. 95 Zhong Guan Cun East Road, Haidian District, Beijing 100191, China

Shengcai Liao

Chinese Academy of Sciences, Center for Biometrics and Security Research & National Laboratory of Pattern Recognition, Institute of Automation, No. 95 Zhong Guan Cun East Road, Haidian District, Beijing 100191, China

Stan Z. Li

Chinese Academy of Sciences, Center for Biometrics and Security Research & National Laboratory of Pattern Recognition, Institute of Automation, No. 95 Zhong Guan Cun East Road, Haidian District, Beijing 100191, China

J. Electron. Imaging. 22(4), 043035 (Dec 19, 2013). doi:10.1117/1.JEI.22.4.043035
History: Received August 22, 2013; Revised October 30, 2013; Accepted November 19, 2013
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Abstract.  An occlusion robust image representation method is presented and applied to face recognition. In our method, Gabor phase difference representation is used mainly to resist occlusion. Based on the good ability of Gabor filters to capture image structure and the robustness to image occlusion shown here, Gabor phase features are expected to be discriminative and robust for face representation in occlusion case. Furthermore, we find that different scales and orientations of Gabor phase features lead to quite varied performance and then we analyze it carefully and find the effective Gabor phase (EGP) features. Moreover, we adopt spectral regression–based discriminant analysis, along with the extracted EGP features, to find the most discriminant subspace for classification. Thereby, an occlusion robust face image discriminant subspace is derived. Five kinds of feature representation methods and two subspace learning methods are compared for our recognition problem. Extensive experiments with various occlusion cases show the efficacy of the proposed method.

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Citation

Hong Han ; Jianfei Zhu ; Zhen Lei ; Shengcai Liao and Stan Z. Li
"Discriminant analysis with Gabor phase feature for robust face recognition", J. Electron. Imaging. 22(4), 043035 (Dec 19, 2013). ; http://dx.doi.org/10.1117/1.JEI.22.4.043035


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