19 December 2013 Discriminant analysis with Gabor phase feature for robust face recognition
Hong Han, Jianfei Zhu, Zhen Lei, Shengcai Liao, Stan Z. Li
Author Affiliations +
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.
© 2013 SPIE and IS&T 0091-3286/2013/$25.00 © 2013 SPIE and IS&T
Hong Han, Jianfei Zhu, Zhen Lei, Shengcai Liao, and Stan Z. Li "Discriminant analysis with Gabor phase feature for robust face recognition," Journal of Electronic Imaging 22(4), 043035 (19 December 2013). https://doi.org/10.1117/1.JEI.22.4.043035
Published: 19 December 2013
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CITATIONS
Cited by 3 scholarly publications.
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KEYWORDS
Facial recognition systems

Databases

Principal component analysis

Eyeglasses

Image filtering

Feature extraction

Detection and tracking algorithms

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