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A novel sparse representation method based on virtual samples for face recognition

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Abstract

Though sparse representation (Wagner et al. in IEEE Trans Pattern Anal Mach Intell 34(2):372–386, 2012, CVPR 597–604, 2009) can perform very well in face recognition (FR), it still can be improved. To improve the performance of FR, a novel sparse representation method based on virtual samples is proposed in this paper. The proposed method first extends the training samples to form a new training set by adding random noise to them and then performs FR. As the testing samples can be represented better with the new training set, the ultimate classification obtained using the proposed method is more accurate than the classification based on the original training samples. A number of FR experiments show that the classification accuracy obtained using our method is usually 2–5 % greater than that obtained using the method mentioned in Xu and Zhu (Neural Comput Appl, 2012).

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References

  1. Wagner A, Wright J, Ganesh A, Zhou Z, Mobahi H, Ma Y (2012) Toward a practical face recognition system: robust alignment and illumination by sparse representation. IEEE Trans Pattern Anal Mach Intell 34(2):372–386

    Article  Google Scholar 

  2. Wagner A, Wright J, Ganesh, Zhou Z, Ma Y (2009) Towards a practical face recognition system: robust registration and illumination by sparse representation. CVPR 597–604

  3. Xu Y, Zhu Q (2012) A simple and fast representation-based face recognition method. Neural Comput Appl doi:10.1007/s00521-012-0833-5

  4. Schölkopf B, Smola A, Müller K-R (1997) Kernel principal component analysis. In: Proceedings of the Artificial Neural Networks: ICANN’97, Lecture Notes in Computer Science, Vol 1327/1997, pp 583–588, doi:10.1007/BFb0020217

  5. Pentland A (2000) Looking at people: sensing for ubiquitous and wearable computing. IEEE Trans Pattern Anal Mach Intell 22(1):107–119

    Article  Google Scholar 

  6. Cho S, Chow T (2006) Robust face recognition using generalized neural reflectance model. Neural Comput Appl 15(2):170–182. doi:10.1007/s00521-005-0017-7.7

    Article  Google Scholar 

  7. Cho S, Wong J (2008) Human face recognition by adaptive processing of tree structures representation. Neural Comput Appl 17(3):201–215

    Article  Google Scholar 

  8. Xu Y, Zhang D, Yang J (2008) An approach for directly extracting features from matrix data and its application in face recognition. Neurocomputing 71(10–12):1857–1865

    Article  Google Scholar 

  9. Khashman A (2009) Application of an emotional neural network to facial recognition. Neural Comput Appl 18(4):309–320

    Article  Google Scholar 

  10. Li JB, Pan JS, Lu ZM (2009) Kernel optimization-based discriminant analysis for face recognition. Neural Comput Appl 18(6):603–612

    Article  Google Scholar 

  11. Sun N, Ji Z, Zou C, Zhao L (2010) Two-dimensional canonical correlation analysis and its application in small sample size face recognition. Neural Comput Appl 19(3):377–382

    Article  Google Scholar 

  12. Sharma A, Dubey A, Jagannatha AN, Anand RS (2010) Pose invariant face recognition based on hybrid-global linear regression. Neural Comput Appl 19(8):1227–1235

    Article  Google Scholar 

  13. Ebied HM, Revett K, Tolba MF (2012) Evaluation of unsupervised feature extraction neural networks for face recognition. Neural Comput Appl doi:10.1007/s00521-012-0889-2

  14. Xue M, Liu W, Liu X (2012) A novel weighted fuzzy LDA for face recognition using the genetic algorithm. Neural Comput Appl doi:10.1007/s00521-012-0962-x

  15. Liu Z, Zhao H, Pu J, Wang H (2012) Face recognition under varying illumination, Neural Comput Appl doi:10.1007/s00521-012-1042-y

  16. Yang N, He R, Zheng WS, Wang X (2012) Robust large margin discriminant tangent analysis for face recognition. Neural Comput Appl 21(2):269–279

    Article  Google Scholar 

  17. Sirovich L, Kirby M (1987) Low-dimensional procedure for characterization of human faces. J Opt Soc Am A: 4(3):519–526

    Article  Google Scholar 

  18. Kirby M, Sirovich L (1990) Application of the KL procedure for the characterization of human faces. IEEE Trans Pattern Anal Mach Intell 12(1):103–108

    Article  Google Scholar 

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

    Article  Google Scholar 

  20. Yang J, Yang JY (2002) From image vector to matrix: a straightforward image projection technique: IMPCA versus PCA. Pattern Recogn 35(9):1999

    Article  Google Scholar 

  21. Yang J, Zhang D, Frangi AF, Yang JY (2004) Two-dimensional PCA: a new approach to appearance-based face representation and recognition. IEEE Trans Pattern Anal Mach Intell 26(1):131–137

    Article  Google Scholar 

  22. Wang H, Chen S, Hu Z, Luo B (2008) Probabilistic two-dimensional principal component analysis and its mixture model for face recognition. Neural Comput Appl 17(5–6):541–547. doi:10.1007/s00521-007-0155-1

    Article  Google Scholar 

  23. Sun N, Wang H, Ji Z, Zou C, Zhao L (2008) An efficient algorithm for Kernel two-dimensional principal component analysis. Neural Comput Appl 17(1):59–64

    Article  Google Scholar 

  24. Xu Y, Zhang D, Yang J (2010) A feature extraction method for use with bimodal biometrics. Pattern Recogn 43(3):1106–1115

    Article  MATH  Google Scholar 

  25. Yang W, Sun C, Ricanek K, Yang W, Sun C, Ricanek K (2012) Sequential row–column 2DPCA for face recognition. Neural Comput Appl 21(7):1729–1735. doi:10.1007/s00521-011-0676-5

    Article  Google Scholar 

  26. Zhu Q, Xu Y (2012) Multi-directional two-dimensional PCA with matching score level fusion for face recognition. Neural Comput Appl doi:10.1007/s00521-012-0851-3

  27. Mika S, Ratsch G, Weston J, Bernhard. Schölkopf, Müller K-R (1999) Fisher discriminant analysis with kernels. In: Proceedings of the IEEE neural networks for signal processing workshop, pp 41–48

  28. Xu Y, Yang J, Jin Z (2004) A novel method for Fisher discriminant analysis. Pattern Recogn 37(2):381–384

    Article  MATH  Google Scholar 

  29. Yang J, Zhang D, Xu Y, Yang J (2005) Two-dimensional discriminant transform for face recognition. Pattern Recogn 38(7):1120–1129

    Google Scholar 

  30. Wang H, Li P, Zhang T (2008) Histogram feature-based Fisher linear discriminant for face detection. Neural Comput Appl 17(1):49–58

    Article  Google Scholar 

  31. Li J, Pan J, Lu Z (2009) Kernel optimization-based discriminant analysis for face recognition. Neural Comput Appl 18(6):603–612

    Article  Google Scholar 

  32. Li JB, Pan JS, Lu ZM (2009) Face recognition using Gabor-based complete Kernel Fisher discriminant analysis with fractional power polynomial models. Neural Comput Appl 18(6):613–621

    Article  Google Scholar 

  33. Zhang B, Qiao Y (2010) Face recognition based on gradient Gabor feature and Efficient Kernel Fisher analysis. Neural Comput Appl 19(4):617–623

    Article  MathSciNet  Google Scholar 

  34. Wang J, Yang W, Yang J (2012) Face recognition using fuzzy maximum scatter discriminant analysis. Neural Comput Appl doi:10.1007/s00521-012-1020-4

  35. Yang N, He R, Zheng W, Wang X (2012) Robust large margin discriminant tangent analysis for face recognition. Neural Comput Appl 21(2):269–279

    Article  Google Scholar 

  36. Dikmen M, Huang TS (2008) Robust estimation of foreground in surveillance videos by sparse error estimation. ICPR, pp 1–4

  37. Elhamifar E, Vidal R (2009) Sparse subspace clustering. In: Proceedings of IEEE international conference on computer vision and pattern recognition, pp. 2790–2797

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

    Article  Google Scholar 

  39. Mairal J, Bach F, Ponce J, Sapiro G, Zisserman A (2009) Supervised dictionary learning. In: Proceedings of the Advances in NIPS, Vol 21

  40. Lai Z, Jin Z, Yang J, Wong WK (2010) Sparse local discriminant projections for feature extraction. In: Proceedings of ICPR, pp 926–929

  41. Wright J, Ma Y, Mairal J, Sapiro G, Huang TS, Yan S (2010) Sparse representation for computer vision and pattern recognition. Proc IEEE 98(6):1031–1044

    Article  Google Scholar 

  42. Xu Y, Zuo W, Fan Z (2012) Supervised sparse representation method with a heuristic strategy and face recognition experiments. Neurocomputing 79(1):125–131

    Article  Google Scholar 

  43. Zhang L, Yang M, et al. (2011) Sparse representation or collaborative representation: which helps face recognition? In: Proceedings of the IEEE international conference on computer vision, pp 471–478

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

    Article  Google Scholar 

  45. Zhu N, Lv K (2012) A novel two-phase sparse representation method and recognition experiments. Int J Adv Comput Technol 4(9):333–339

    Google Scholar 

  46. (Online). Available: http://cvc.yale.edu/projects/yalefaces/yalefaces.html

  47. (Online). Available: http://cobweb.ecn.purdue.edu/~aleix/aleix_face_DB.html

  48. Xu Y, Jin Z (2008) Down-sampling face images and low-resolution face recognition. In: Proceedings of the 3rd international conference on innovative computing, information and control, pp 392–395

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Authors and Affiliations

  1. Changsha, China

    Deyan Tang, Ningbo Zhu, Fu Yu & Ting Tang

  2. Wuhan, China

    Wei Chen

Authors
  1. Deyan Tang
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  2. Ningbo Zhu
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  3. Fu Yu
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  4. Wei Chen
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  5. Ting Tang
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Correspondence to Deyan Tang.

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Tang, D., Zhu, N., Yu, F. et al. A novel sparse representation method based on virtual samples for face recognition. Neural Comput & Applic 24, 513–519 (2014). https://doi.org/10.1007/s00521-012-1252-3

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