Nothing Special   »   [go: up one dir, main page]
Skip to main content
Springer Nature Link
Log in

Heteroscedastic Sparse Representation Based Classification for Face Recognition

  • Published:
Neural Processing Letters Aims and scope Submit manuscript

Abstract

Sparse representation based classification (SRC) have received a great deal of attention in recent years. The main idea of SRC is to represent a given test sample as a sparse linear combina-tion of all training samples, then classifies the test sample by evaluating which class leads to the minimum residual. Although SRC has achieved good performance, especially in dealing with face occlusion and corruption, it must need a big occlusion dictionary which makes computation very expensive. In this paper, a novel method, called heteroscedastic sparse representation based classification (HSRC), is proposed to address this problem. In the presence of noises, the SRC model exists heteroscedasticity, which makes residual estimation inefficient. Therefore, heteroscedastic correction must be carried out for homoscedasticity by weighting various residuals with heteroscedastic estimation. As for heteroscedasticity, this paper establishes generalized Gaussian model through which to estimate. The proposed HSRC method is applied to face recognition (on the AR and Extended Yale B face databases). The experimental results show that HSRC has significantly less complexity than SRC, while it is more robust.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

References

  1. Aiazzi B, Alparone L, Baronti S (1999) Estimation based on entropy matching for generalized Gaussian pdf modeling. IEEE Signal Process Lett 6(6): 138–140

    Article  Google Scholar 

  2. Chen SS, Donoho DL, Saunders MA (1998) Atomic decomposition by basis pursuit. SIAM J Sci Comput 20(1): 33–61

    Article  MathSciNet  Google Scholar 

  3. Chen SS, Donoho DL, Saunders MA (1999) Atomic decomposition by basis pursuit. SIAM J Sci Comput 20(1): 33–61

    Article  MathSciNet  MATH  Google Scholar 

  4. Chung F (1997) Spectral graph theory. Regional conference series in mathematics, no. 92

  5. Donoho D (2006) Compressed sensing. IEEE Trans Inform Theory 52(4): 1289–1306

    Article  MathSciNet  Google Scholar 

  6. Elad M, Aharon M (2006) Image denoising via sparse and redundant representations over learned dictionaries. IEEE Trans Image Process 15(12): 3736–3745

    Article  MathSciNet  Google Scholar 

  7. Francois M, Pierre V, Jean-Philippe T (2006) Matching pursuit-based shape representation and recognition using scale-space. Int J Imaging Syst Technol 6(5): 162–180

    Google Scholar 

  8. Georghiades A, Belhumeur P, Kriegman D (2001) From few to many: illumination cone models for face recognition under variable lighting and pose. IEEE Trans Pattern Anal Mach Intell 23(6): 643–660

    Article  Google Scholar 

  9. Gragg JG (1983) More efficient estimation in the presence of heteroscedasticity of unknown form. Econometrica 51(3): 751–764

    Article  MathSciNet  Google Scholar 

  10. Gribonval R, Vandergheynst P (2006) On the exponential convergence of matching pursuits in quasi-incoherent dictionaries. IEEE Trans Inform Theory 52(1): 255–261

    Article  MathSciNet  Google Scholar 

  11. He X, Niyogi P (2003) Locality preserving projections. In: Proceedings of 16th conference neural information processing systems

  12. Huang J, Yuen PC, Chen W (2007) Choosing parameters of kernel subspace LDA for recognition of face images under pose and illumination variations. IEEE Trans Syst Man Cybern B Cybern 37: 847–862

    Article  Google Scholar 

  13. Joliffe I (1986) Principal component analysis. Springer, New York

    Google Scholar 

  14. Jonathon Phillips P (1998) Matching pursuit filters applied to face identification. IEEE Trans Image Process 7(8): 1150–1164

    Article  Google Scholar 

  15. Kong H, Wang L, Teoh EK (2005) A framework of 2D fisher discriminant analysis: application to face recognition with small number of training samples. Proc IEEE Int Conf Comput Vis Pattern Recognit 2: 1083–1088

    Google Scholar 

  16. Li H, Jiang T, Zhang K (2004) Efficient and robust feature extraction by maximum margin criterion. In: Proceedings of the advances in neural information processing systems

  17. Long JS, Ervin LH (2000) Using heteroscedasity consistent stand errors in the linear regression model. Am Stat 54: 217–224

    Google Scholar 

  18. Mairal J, Elad M, Sapiro G (2008) Sparse representation for color image restoration. IEEE Trans Image Process 17(1): 53–69

    Article  MathSciNet  Google Scholar 

  19. Mallat S, Zhang Z (1993) Matching pursuits with time-frequency dictionaries. IEEE Trans Signal Process 41(12): 3397–3415

    Article  MATH  Google Scholar 

  20. Martinez A, Benavente R (1998) The AR face database. CVC Tech. Report No. 24

  21. Meignen S, Meignen H (2006) On the modeling of small sample distributions with generalized Gaussian density in a maximum likelihood framework. IEEE Trans Image Process 15(6): 1647–1652

    Article  MathSciNet  Google Scholar 

  22. Muller HG, Stadtmuller U (1987) Estimation of heteroscedasticity in regression analysis. Ann Stat 15(2): 610–625

    Article  MathSciNet  Google Scholar 

  23. Murray J, Kreutz-Delgado K (2007) Visual recognition and inference using dynamic overcomplete sparse learning. Neural Comput 19: 2301–2352

    Article  MathSciNet  MATH  Google Scholar 

  24. Patel VM, Nasrabadi NM, Chellappa R (2010) Automatic target recognition based on simultaneous sparse representation. In: International Conference on Image Processing, pp 1377–1380

  25. Rigamonti R, Brown M, Lepetit V (2011) Are sparse representations really relevant for image classification? In: Computer vision and pattern recognition, pp 1545–1552

  26. Sharifi K (1995) Estimation of shape parameter for generalized Gaussian distributions in subband decompositions of video. IEEE Trans Circuits Syst Video Technol 5: 52–56

    Article  Google Scholar 

  27. Shi Q, Eriksson A, Hengel A (2011) Is face recognition really a compressive sensing problem? In: Computer vision and pattern recognition, pp 553–560

  28. Tropp JA (2004) Greed is good: algorithmic results for sparse approximation. IEEE Trans Inform Theory 50(10): 2231–2242

    Article  MathSciNet  Google Scholar 

  29. Varanasi MK, Aazhang B (1989) Parametric generalized Gaussian density estimation. J Acoust Soc Am 86: 1404–1415

    Article  Google Scholar 

  30. Wright J, Yang A, Ma Y (2009) Robust face recognition via sparse representation. IEEE T-PAMI 31(2): 210–227

    Article  Google Scholar 

  31. Yang M, Zhang L (2010) Gabor feature based sparse representation for face recognition with Gabor occlusion dictionary. In: Europeon conference on computer vision

  32. Yang A, Wright J, Ma Y (2007) Feature selection in face recognition: a sparse representation perspective. UC Berkeley Technical Report UCB/EECS-2007-99

  33. Zhang L (2011) Sparse representation or collaborative representation which helps face. In: International conference on computer vision

Download references

Author information

Authors and Affiliations

  1. School of Computer Science and Technology, Nanjing University of Science and Technology, Nanjing, 210094, People’s Republic of China

    Hao Zheng, Jianchun Xie & Zhong Jin

  2. School of Mathematics and Information Technology, Nanjing Xiao Zhuang University, Nanjing, 211171, People’s Republic of China

    Hao Zheng

Authors
  1. Hao Zheng
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jianchun Xie
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Zhong Jin
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Hao Zheng.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Zheng, H., Xie, J. & Jin, Z. Heteroscedastic Sparse Representation Based Classification for Face Recognition. Neural Process Lett 35, 233–244 (2012). https://doi.org/10.1007/s11063-012-9214-4

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11063-012-9214-4

Keywords

Search

Navigation