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Orthogonal discriminant improved local tangent space alignment based feature fusion for face recognition

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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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Authors and Affiliations

  1. School of Electronic Information and Electrical Engineering, Shanghai Jiaotong University, Shanghai, 200240, China

    Qiang Zhang  (张 强), Yun-ze Cai  (蔡云泽) & Xiao-ming Xu  (许晓鸣)

  2. University of Shanghai for Science and Technology, Shanghai, 200093, China

    Xiao-ming Xu  (许晓鸣)

  3. Shanghai Academy of Systems Science, Shanghai, 200093, China

    Xiao-ming Xu  (许晓鸣)

Authors
  1. Qiang Zhang  (张 强)
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  2. Yun-ze Cai  (蔡云泽)
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  3. Xiao-ming Xu  (许晓鸣)
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Corresponding author

Correspondence to Qiang Zhang  (张 强).

Additional information

Foundation item: the National Natural Science Foundation of China (No. 61004088), and the Key Basic Research Foundation of Shanghai Municipal Science and Technology Commission (No. 09JC1408000)

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Zhang, Q., Cai, Yz. & Xu, Xm. Orthogonal discriminant improved local tangent space alignment based feature fusion for face recognition. J. Shanghai Jiaotong Univ. (Sci.) 18, 425–433 (2013). https://doi.org/10.1007/s12204-013-1417-y

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  • DOI: https://doi.org/10.1007/s12204-013-1417-y

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