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Collaborative representation analysis methods for feature extraction

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Abstract

Recently, sparse representation (SR) theory gets much success in the fields of pattern recognition and machine learning. Many researchers use SR to design classification methods and dictionary learning via reconstruction residual. It was shown that collaborative representation (CR) is the key part in sparse representation-based classification (SRC) and collaborative representation-based classification (CRC). Both SRC and CRC are good classification methods. Here, we give a collaborative representation analysis (CRA) method for feature extraction. Not like SRC-/CRC-based methods (e.g., SPP and CRP), CRA could directly extract the features like PCA and LDA. Further, a Kernel CRA (KCRA) is developed via kernel tricks. The experimental results on FERET and AR face databases show that CRA and KCRA are two effective feature extraction methods and could get good performance.

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References

  1. Zhao W, Chellappa R, Phillips PJ et al (2003) Face recognition: a literature survey [J]. ACM Comput Surv 35(4):399–459

    Article  Google Scholar 

  2. Turk M, Pentland A (1991) Eigenfaces for recognition. J Cogn Neurosci 3(1):71–86

    Article  Google Scholar 

  3. Belhumeur V, Hespanha J, Kriegman D (1997) Eigenfaces vs. fisherfaces: recognition using class specific linear projection. IEEE Trans Pattern Anal Mach Intell 19(7):711–720

    Article  Google Scholar 

  4. Raudys SJ, Jain AK (1991) Small sample size effects in statistical pattern recognition: recommendations for practitioners. IEEE Trans Pattern Anal Mach Intell 13(3):252–264

    Article  Google Scholar 

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

    Article  Google Scholar 

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

    Article  MathSciNet  Google Scholar 

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

    Article  MATH  Google Scholar 

  8. Xu Y, Yang JY, Jin Z (2003) Theory analysis on FSLDA and ULDA. Pattern Recognit 36(12):3031–3033

    Article  MATH  Google Scholar 

  9. Friedman JH (1989) Regularized discriminant analysis. J Am Stat Assoc 84:165–175

    Article  MathSciNet  Google Scholar 

  10. Hastie T, Tibshirani R (1995) Penalized discriminant analysis. Ann Stat 23:73–102

    Article  MathSciNet  MATH  Google Scholar 

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

    Article  MATH  Google Scholar 

  12. Li H, Jiang T, Zhang K (2006) Efficient and robust feature extraction by maximum margin criterion. IEEE Trans Neural Netw 17(1):1157–1165

    Article  Google Scholar 

  13. Schölkopf B, Smola A, Müller KR (1998) Nonlinear component analysis as a kernel eigenvalue problem. Neural Comput 10(5):1299–1319

    Article  Google Scholar 

  14. Mika S, Rätsch G, Weston J, Schölkopf B, Müller K-R (1999) Fisher discriminant analysis with kernels. In: Proc. IEEE Int’l Workshop Neural Networks for Signal Processing IX, 199: 41–48

  15. Yang J, Frangi AF, Yang JY, Zhang D (2005) KPCA Plus LDA: a complete kernel fisher discriminant frame work for feature extraction and recognition. IEEE Trans Pattern Anal Mach Intell 27(2):230–244

    Article  Google Scholar 

  16. Tenenbaum JB, de Silva V, Langford JC (2000) A global geometric framework for nonlinear dimensionality reduction. Science 290:2319–2323

    Article  Google Scholar 

  17. Roweis ST, Saul LK (2000) Nonlinear dimension reduction by locally linear embedding. Science 290:2323–2326

    Article  Google Scholar 

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

    Article  MATH  Google Scholar 

  19. He X, Yan S, Hu Y, Niyogi P, Zhang H (2005) Face recognition using laplacianfaces. IEEE Trans Pattern Anal Mach Intell 27(3):328–340

    Article  Google Scholar 

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

    Article  Google Scholar 

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

    Article  MATH  Google Scholar 

  22. Yan S, Xu D, Zhang B, Zhang HJ (2007) Graph embedding and extensions: a general framework for dimensionality reduction. IEEE Trans Pattern Anal Mach Intell 29(1):40–51

    Article  Google Scholar 

  23. Yang W, Sun C, Zhang L (2011) A multi-manifold discriminant analysis method for image feature extraction. Pattern Recognit 44(8):1649–1657

    Article  MATH  Google Scholar 

  24. Xu Y, Zhu X et al (2013) Using the original and symmetrical face training samples to perform representation based two-step face recognition. Pattern Recognit 46:1151–1158

    Article  Google Scholar 

  25. Wright J, Yang A, Sastry S, Ma Y (2009) Robust face recognition via sparse representation. IEEE Trans Pattern Anal Mach Intell 31(2):210–227

    Article  Google Scholar 

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

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

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

    Article  MathSciNet  MATH  Google Scholar 

  29. Wang H, Yuan C, Hu W et al (2014) Action recognition using nonnegative action component representation and sparse basis selection. IEEE Trans Image Process 23(2):570–581

    Article  MathSciNet  MATH  Google Scholar 

  30. Liu W, Tao D, Cheng J, Tang Y (2014) Multiview Hessian discriminative sparse coding for image annotation. Comput Vis Image Underst 118:50–60

    Article  Google Scholar 

  31. Liu W, Song C, Wang Y (2012) Facial expression recognition based on discriminative dictionary learning, ICPR

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

    Article  MATH  Google Scholar 

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

    Article  MathSciNet  MATH  Google Scholar 

  34. Yang J, Chu D, Zhang L, Xu Y, Yang J (2013) Sparse representation classifier steered discriminative projection with application to face recognition. IEEE Trans Neural Netw Learn Syst 24(7):1023–1034

    Article  Google Scholar 

  35. Clemmensen L, Hastie T, Witten D, Ersboll B (2011) Sparse discriminant analysis. Technometrics 53(4):406–413

    Article  MathSciNet  Google Scholar 

  36. Lai Z, Wan M, Jin Z, Yang J (2011) Sparse two-dimensional local discriminant projections for feature extraction. Neurocomputing 74(4):629–637

    Article  Google Scholar 

  37. Lai Z, Wong WK, Jin Z, Yang J, Xu Y (2012) Sparse approximation to the eigen subspace for discrimination. IEEE Trans Neural Netw Learn Syst 23(12):1948–1960

    Article  Google Scholar 

  38. Xu Y, Zhang D, Yang J, Yang J-Y (2011) A two-phase test sample sparse representation method for use with face recognition. IEEE Trans Circuits Syst Video Technol 21(9):1255–1262

    Article  MathSciNet  Google Scholar 

  39. Xu Y, Zhu Q (2013) A simple and fast representation-based face recognition method. Neural Comput Appl 22(7–8):1543–1549

    Article  Google Scholar 

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

  41. Yang W, Wang Z, Sun C (2015) A collaborative representation based projections method for feature extraction. Pattern Recognit 48(1):20–27

    Article  Google Scholar 

  42. Phillips PJ, Moon H, Rizvi SA, Rauss PJ (2000) The FERET evaluation methodology for face recognition algorithms. IEEE Trans Pattern Anal Mach Intell 22(10):1090–1104

    Article  Google Scholar 

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

  44. Martinez AM, Benavente R. The AR Face Database http://cobweb.ecn.purdue.edu/~aleix/aleix_face_DB.html

  45. Martinez AM, Benavente R. The AR Face Database, CVC Technical Report #24, June 1998

Download references

Acknowledgments

This project is partly supported by NSF of China (31200747), the Jiangsu Key Laboratory of Image and Video Understanding for Social Safety (Nanjing University of Science and Technology) (No. 30920130122006).

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Authors and Affiliations

  1. School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, 210094, China

    Juliang Hua, Huan Wang, Mingu Ren & Heyan Huang

  2. Jiangsu Key Laboratory of Image and Video Understanding for Social Safety, Nanjing University of Science and Technology, Nanjing, 210094, China

    Juliang Hua, Huan Wang, Mingu Ren & Heyan Huang

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  1. Juliang Hua
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  2. Huan Wang
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  3. Mingu Ren
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  4. Heyan Huang
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Correspondence to Juliang Hua.

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Hua, J., Wang, H., Ren, M. et al. Collaborative representation analysis methods for feature extraction. Neural Comput & Applic 28 (Suppl 1), 225–231 (2017). https://doi.org/10.1007/s00521-016-2299-3

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  • DOI: https://doi.org/10.1007/s00521-016-2299-3

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