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Face recognition using an NNSRM classifier in LDA subspace

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Abstract

NNSRM is an implementation of the structural risk minimization (SRM) principle using the nearest neighbor (NN) rule, and linear discriminant analysis (LDA) is a dimension-reducing method, which is usually used in classifications. This paper combines the two methods for face recognition. We first project the face images into a PCA subspace, then project the results into a much lower-dimensional LDA subspace, and then use an NNSRM classifier to recognize them in the LDA subspace. Experimental results demonstrate that the combined method can achieve a better performance than NN by selecting different distances and a comparable performance with SVM but costing less computational time.

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Notes

  1. The Yale database is available from http://cvc.yale.edu/ projects/yalefaces/yale-faces.html

  2. The AR database of is available from http://rvl1.ec. purdue.edu/ARdatabase/AR-database.html.

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Authors and Affiliations

  1. Department of Computer Science and Technology, Tsinghua University, Beijing, China

    Danian Zheng, Meng Na & Jiaxin Wang

  2. Tsinghua University, FIT Building, Room 1-509, Beijing, 100084, China

    Jiaxin Wang

Authors
  1. Danian Zheng
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  2. Meng Na
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  3. Jiaxin Wang
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Correspondence to Jiaxin Wang.

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Zheng, D., Na, M. & Wang, J. Face recognition using an NNSRM classifier in LDA subspace. Pattern Anal Applic 10, 375–381 (2007). https://doi.org/10.1007/s10044-007-0067-9

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  • DOI: https://doi.org/10.1007/s10044-007-0067-9

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