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Chinese Conference on Pattern Recognition

CCPR 2012: Pattern Recognition pp 276–283Cite as

Collaborative Representation Based Projections for Face Recognition

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Part of the book series: Communications in Computer and Information Science ((CCIS,volume 321))

Abstract

In this paper, we develop a collaborative representation based projections (CRP) for face recognition, which is an unsupervised method. Like SPP and NPE, CRP aims to preserve the sparse reconstruction relations of data. CRP is much faster than SPP since CRP adopts collaborative representation with regularized least square related as objective function while SPP adopts sparse representation related as objective function. Experimental results on ORL and FERET demonstrate that CRP works well in feature extraction and leads to good recognition performance.

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References

  1. Smith, T.F., Waterman, M.S.: Identification of Common Molecular Subsequences. J. Mol. Biol. 147, 195–197 (1981)

    Article  Google Scholar 

  2. Raudys, S.J., Jain, A.K.: Small sample size effects in statistical pattern recognition: recommendations for practitioners. IEEE Trans. Pattern Ana1ysis and Machine Intel1igence 13(3), 252–264 (1991)

    Article  Google Scholar 

  3. Swets, D.L., Weng, J.: Using discriminant eigenfeatures for image retrieval. IEEE Trans. on Pattern Analysis and Machine Intelligence 18(8), 831–836 (1996)

    Article  Google Scholar 

  4. Belhumeur, V., Hespanha, J., Kriegman, D.: Eigenfaces vs Fisherfaces: recognition using class specific linear projection. IEEE Trans. Pattern Analysis and Machine Intelligence 19(7), 711–720 (1997)

    Article  Google Scholar 

  5. Yang, J., Yang, J.: Why can LDA be performed in PCA transformed space? Pattern Recognition 36(2), 563–566 (2003)

    Article  Google Scholar 

  6. Hong, Z., Yang, J.: Optimal discriminant plane for a small number of samples and design method of classifier on the plane. Pattern Recognition 24(4), 317–324 (1991)

    Article  MathSciNet  Google Scholar 

  7. Friedman, J.H.: Regularized discriminant analysis. Journal of the American Statistical Association 84, 165–175 (1989)

    Article  MathSciNet  Google Scholar 

  8. Hastie, T., Tibshirani, R.: Penalized discriminant analysis. The Annals of Statistics 23, 73–102 (1995)

    Article  MathSciNet  MATH  Google Scholar 

  9. Chen, L.F., Liao, H.Y.M., Ko, M.T., Yu, G.J.: A new LDA-based face recognition system which can solve the small sample size problem. Pattern Recognition 33(1), 1713–1726 (2000)

    Article  Google Scholar 

  10. Yu, H., Yang, J.: A direct LDA algorithm for high dimensional data–with application to face recognition. Pattern Recognition 34(10), 2067–2070 (2001)

    Article  MATH  Google Scholar 

  11. Gao, Q., Zhang, L., Zhang, D.: Face recognition using FLDA with single training image per-person. Applied Mathematics and Computation 205(12), 726–734 (2008)

    Article  MATH  Google Scholar 

  12. Zhuang, X., Dai, D.: Inverse fisher discriminant criteria for small sample size problem and its application to face recognition. Pattern Recognition 38(11), 2192–2194 (2005)

    Article  Google Scholar 

  13. Jin, Z., Yang, J., Hu, Z., Lou, Z.: Face recognition based on the uncorrelated discrimination Transformation. Pattern Recognition 34(7), 1405–1416 (2001)

    Article  MATH  Google Scholar 

  14. Yang, J., Liu, C.: Color Image Discrimiant Models and Algorithms for Face Recognition. IEEE Trans. Neural Networks 19(2), 2088–2098 (2008)

    Article  Google Scholar 

  15. Loog, M.: Approximate Pairwise Accuracy Criterion for Multiclass Linear Dimension Reduction: Generalization of the Fisher Criterion. IEEE Trans. Pattern Analysis and Machine Intelligence 26(7), 762–766 (2001)

    Article  Google Scholar 

  16. Lotlikar, R., Kothari, R.: Fractional-Step Dimensionality Reduction. IEEE Trans. Pattern Recognition and Machine Intelligence 22(6), 623–627 (2000)

    Article  Google Scholar 

  17. Lu, J., Plataniotic, K.N., Venetsanopouls, A.N.: Face Recognition Using LDA-Based Algorithm. IEEE Trans. Neural Networks 14(1), 195–200 (2003)

    Article  Google Scholar 

  18. Tao, D., Li, X., Wu, X., Maybank, S.J.: Geometric Mean for Subspace Selection. IEEE Trans. Pattern Analysis and Machine Intelligence 31(2), 260–274 (2009)

    Article  Google Scholar 

  19. Xu, Y., Zhang, D., Yang, J., Yang, J.-Y.: A two-phase test sample sparse representation method for use with face recognition. IEEE Transactions on Circuits and Systems for Video Technology 21(9), 1255–1262 (2011)

    Article  Google Scholar 

  20. Yang, W., Wang, J., Ren, M., Yang, J.: Feature extraction based on laplacian bidirectional maximum margin criterion. Pattern Recognition 42(11), 2327–2334 (2009)

    Article  MATH  Google Scholar 

  21. Yang, W., Wang, J., Ren, M., Zhang, L., Yang, J.: Feature extraction using fuzzy inverse FDA. Neurocomputing 72(13-15), 3384–3390 (2009)

    Article  Google Scholar 

  22. Tenenbaum, J.B., de Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290, 2319–2323 (2000)

    Article  Google Scholar 

  23. Roweis, S.T., Saul, L.K.: Nonlinear dimension reduction by locally linear embedding. Science 290, 2323–2326 (2000)

    Article  Google Scholar 

  24. Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural Computation 15(6), 1373–1396 (2003)

    Article  MATH  Google Scholar 

  25. He, X., Yan, S., Hu, Y., Niyogi, P., Zhang, H.: Face recognition using laplacianfaces. IEEE Trans. Pattern Analysis and Machine Intelligence 27(3), 328–340 (2005)

    Article  Google Scholar 

  26. Xu, Y., Zhong, A., Yang, J., Zhang, D.: LPP solution schemes for use with face recognition. Pattern Recognition 43(12), 4165–4176 (2010)

    Article  MATH  Google Scholar 

  27. He, X., Cai, D., Yan, S., Zhang, H.: Neighborhood preserving embedding. In: ICCV, pp. 1208–1213 (2005)

    Google Scholar 

  28. Yang, J., Zhang, D., Yang, J., Niu, B.: Globally maximizing, locally minimizing: unsupervised discriminant projection with applications to face and palm biometrics. IEEE Trans. Pattern Analysis and Machine Intelligence 29(4), 650–664 (2007)

    Article  Google Scholar 

  29. Yan, S., Xu, D., Zhang, B., Zhang, H.: Graph embedding and extensions: a general framework for dimensionality reduction. IEEE Trans. Pattern Analysis and Machine Intelligence 29(1), 40–51 (2007)

    Article  MathSciNet  Google Scholar 

  30. Yang, J., Wright, J., Huang, T., Ma, Y.: Image super-resolution as sparse representation of raw patches. In: CVPR (2008)

    Google Scholar 

  31. Rao, S., Tron, R., Vidal, R., Ma, Y.: Motion segmentation via robust subspace separation in the presence of outlying, incomplete, and corrupted trajectories. In: CVPR (2008)

    Google Scholar 

  32. Mairal, J., Sapiro, G., Elad, M.: Learning multiscale sparse representations for image and video restoration. SIAM MMS 7(1), 214–241 (2008)

    MathSciNet  MATH  Google Scholar 

  33. Wright, J., Yang, A., Sastry, S., Ma, Y.: Robust face recognition via sparse representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 31(2), 210–227 (2009)

    Article  Google Scholar 

  34. Qiao, L.S., Chen, S.C., Tan, X.Y.: Sparsity preserving projections with applications to face recognition. Pattern Recognition 43(1), 331–341 (2010)

    Article  MATH  Google Scholar 

  35. Cheng, B., Yang, J., Yan, S., Fu, Y., Huang, T.: Learning with L1-graph for image analysis. IEEE Transactions on Image Processing 19(4), 858–866 (2010)

    Article  MathSciNet  Google Scholar 

  36. Zhang, L., Yang, M., Feng, X.: Sparse representation or collaborative representation: which helps face recognition. In: ICCV (2011)

    Google Scholar 

  37. Phillips, P.J., Moon, H., Rizvi, S.A., Rauss, P.J.: The FERET evaluation methodology for face recognition algorithms. IEEE Trans. Pattern Ana1ysis and Machine Intelligence 22(10), 1090–1104 (2000)

    Article  Google Scholar 

  38. Phillips, P.J.: The facial recognition technology (FERET) database (2004), http://www.itl.nist.gov/iad/humanid/feret/feret_master.html

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Author information

Authors and Affiliations

  1. School of Automation, Southeast University, Nanjing, 210096, China

    Wankou Yang, Changyin Sun & Qingshan Liu

  2. Face Aging Group, Dept. of Computer Science, UNCW, USA

    Karl Ricanek

Authors
  1. Wankou Yang
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  2. Changyin Sun
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  3. Qingshan Liu
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  4. Karl Ricanek
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Editor information

Editors and Affiliations

  1. Institute of Automation, National Laboratory of Pattern Recognition, Chinese Academy of Sciences, No.95, Zhongguancun East Road, 100190, Beijing, China

    Cheng-Lin Liu

  2. Department of Automation, Tsinghua University, Haidian District, 100084, Beijing, China

    Changshui Zhang

  3. Institute of Automation, National Laboratory of Pattern Recognition, Chinese Academy of Sciences, 100190, Beijing, China

    Liang Wang

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© 2012 Springer-Verlag Berlin Heidelberg

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Yang, W., Sun, C., Liu, Q., Ricanek, K. (2012). Collaborative Representation Based Projections for Face Recognition. In: Liu, CL., Zhang, C., Wang, L. (eds) Pattern Recognition. CCPR 2012. Communications in Computer and Information Science, vol 321. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33506-8_35

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  • DOI: https://doi.org/10.1007/978-3-642-33506-8_35

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-33505-1

  • Online ISBN: 978-3-642-33506-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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