Orthogonal discriminant improved local tangent space alignment based feature fusion for face recognition |
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| Authors: | ZHANG Qiang CAI Yun-ze XU Xiao-ming |
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| Institution: | 1. School of Electronic Information and Electrical Engineering, Shanghai Jiaotong University, Shanghai 200240, China
2. School of Electronic Information and Electrical Engineering, Shanghai Jiaotong University, Shanghai 200240, China;University of Shanghai for Science and Technology, Shanghai 200093, China;Shanghai Academy of Systems Science, Shanghai 200093, China |
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| Abstract: | Improved local tangent space alignment (ILTSA) is a recent nonlinear dimensionality reduction method which can efficiently recover the geometrical structure of sparse or non-uniformly distributed data manifold. In this paper, based on combination of modified maximum margin criterion and ILTSA, a novel feature extraction method named orthogonal discriminant improved local tangent space alignment (ODILTSA) is proposed. ODILTSA can preserve local geometry structure and maximize the margin between different classes simultaneously. Based on ODILTSA, a novel face recognition method which combines augmented complex wavelet features and original image features is developed. Experimental results on Yale, AR and PIE face databases demonstrate the effectiveness of ODILTSA and the feature fusion method. |
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| Keywords: | manifold learning linear extension orthogonal discriminant improved local tangent space alignment(ODILTSA) augmented Gabor-like complex wavelet transform face recognition information fusion |
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