Multimedia Tools and Applications

, Volume 75, Issue 20, pp 12535–12546 | Cite as

Robust regression based face recognition with fast outlier removal

  • Fumin Shen
  • Wankou Yang
  • Hanxi LiEmail author
  • Hanwang Zhang
  • Heng Tao Shen


In this paper, we propose a new robust face recognition method through pixel selection. The method is based on the subspace assumption that a face can be represented by a linear combination in terms of the samples from the same subject. In order to obtain a reliable representation, only a subset of pixels with respect to smallest residuals are taken into the estimation. Outlying pixels which deviate from the linear model of the majority are removed using a robust estimation technique — least trimmed squares regression (LTS). By this method, the representation residual with each class is computed from only the clean data, which gives a more discriminant classification rule. The proposed algorithm provides a novel way to tackle the crucial occlusion problem in face recognition. Evaluation of the proposed algorithm is conducted on several public databases for the cases of both artificial and nature occlusions. The promising results show its efficacy.


Face recognition Robust regression Least trimmed sum of squares 



Wankou Yang was supported by NSFC under project No.61375001.


  1. 1.
    Basri R, Jacobs D (2003) Lambertian reflectance and linear subspaces. IEEE Trans Pattern Anal Mach Intell 25(2):218–233CrossRefGoogle Scholar
  2. 2.
    Deng W, Hu J, Guo J (2012) Extended SRC: undersampled face recognition via intra-class variant dictionary. IEEE Trans Pattern Anal Mach Intell PP(99):1Google Scholar
  3. 3.
    Georghiades AS, Belhumeur PN, Kriegman DJ (2001) From few to many: illumination cone models for face recognition under variable lighting and pose. IEEE Trans Pattern Anal Mac. Intell 23(6):643–660CrossRefGoogle Scholar
  4. 4.
    Hawkins DM, Olive DJ (1999) Improved feasible solution algorithms for high breakdown estimation. Comput Stat Data Anal 30(1):1–11MathSciNetCrossRefzbMATHGoogle Scholar
  5. 5.
    He X, Cai D, Niyogi P. (2005) Laplacian score for feature selection. In: Proc. of advances in neural information processing systemsGoogle Scholar
  6. 6.
    Ho J, Yang MH, Lim J, Lee KC, Kriegman D (2003) Clustering appearances of objects under varying illumination conditions. In: Proc. IEEE int. conf. computer vision and pattern recognition, pp 11–18Google Scholar
  7. 7.
    Hong R, Wang M, Gao Y, Tao D, Li X, Wu X (2013) Image annotation by multiple-instance learning with discriminative feature mapping and selection. IEEE Trans Cybern 44(5):669–680CrossRefGoogle Scholar
  8. 8.
    Hubert M, Rousseeuw PJ, Aelst Sv (2008) High-breakdown robust multivariate methods. Stat Sci 23(1):92–119MathSciNetCrossRefzbMATHGoogle Scholar
  9. 9.
    Lee K, Ho J, Kriegman D (2005) Acquiring linear subspaces for face recognition under variable lighting. IEEE Trans Pattern Anal Mach Intell 27 (5):684–698CrossRefGoogle Scholar
  10. 10.
    Lee KC, Ho J, Kriegman D (2005) Acquiring linear subspaces for face recognition under variable lighting. IEEE Trans Pattern Anal Mach Intell 27(5):684–698CrossRefGoogle Scholar
  11. 11.
    Martinez A, Benavente R (1998) The AR face database. CVC, Tech. RepGoogle Scholar
  12. 12.
    Naseem I, Togneri R, Bennamoun M (2010) Linear regression for face recognition. IEEE Trans Pattern Anal Mach Intell 32 (11):2106–2112CrossRefGoogle Scholar
  13. 13.
    Rousseeuw PJ (1984) Least median of squares regression. J Amer Stat Assoc 79(388)Google Scholar
  14. 14.
    Rousseeuw PJ, Driessen K (2006) Computing LTS regression for large data sets. Data Min Knowl Discov 12:29–45MathSciNetCrossRefzbMATHGoogle Scholar
  15. 15.
    Shen F, Shen C, van den Hengel A, Tang Z (2013) Approximate least trimmed sum of squares fitting and applications in image analysis. IEEE Trans Image Process 22(5):1836–1847MathSciNetCrossRefGoogle Scholar
  16. 16.
    Shen F, Shen C, Hill R, van den Hengel A, Tang Z (2014) Fast approximate l minimization: speeding up robust regression. Comput Stat Data Anal 77(0):25–37MathSciNetCrossRefGoogle Scholar
  17. 17.
    Shen F, Shen C, Shi Q, van den Hengel A, Tang Z (2013) Inductive hashing on manifolds In: IEEE conference on computer vision and pattern recognition (CVPR’13)Google Scholar
  18. 18.
    Shen F, Tang Z, Xu J (2013) Locality constrained representation based classification with spatial pyramid patches. Neurocomputing 101(0):104–115CrossRefGoogle Scholar
  19. 19.
    Shi Q, Eriksson A, van den Hengel A, Shen C (2011) Is face recognition really a compressive sensing problem?. In: Proc. IEEE int. conf. computer vision and pattern recognition, pp 553–560Google Scholar
  20. 20.
    Sim T, Baker S, Bsat M (2003) The CMU pose, illumination, and expression database. IEEE Trans Patt Anal Mach Intell 25:1615–1618CrossRefGoogle Scholar
  21. 21.
    Wang M, Hua XS, Hong R, Tang J, Qi GJ, Song Y (2009) Unified video annotation via multigraph learning. IEEE Trans Circ Syst Video Technol 19(5):733–746CrossRefGoogle Scholar
  22. 22.
    Wang M, Hua XS, Tang J, Hong R (2009) Beyond distance measurement: constructing neighborhood similarity for video annotation. IEEE Trans Multimedia 11(3):465–476CrossRefGoogle Scholar
  23. 23.
    Wang M, Ni B, Hua XS, Chua TS (2012) Assistive tagging: a survey of multimedia tagging with human-computer joint exploration. ACM Comput Surveys 44(4):25CrossRefGoogle Scholar
  24. 24.
    Wright J, Yang AY, Ganesh A, Sastry SS, Ma Y (2009) Robust face recognition via sparse representation. IEEE Trans Pattern Anal Mach Intell 31:210–227CrossRefGoogle Scholar
  25. 25.
    Yang Y, Huang Z, Shen HT, Zhou X (2011) Mining multi-tag association for image tagging. World Wide Web 14(2):133–156CrossRefGoogle Scholar
  26. 26.
    Yang Y, Huang Z, Yang Y, Liu J, Shen HT, Luo J (2013) Local image tagging via graph regularized joint group sparsity. Pattern Recog 46(5):1358–1368CrossRefzbMATHGoogle Scholar
  27. 27.
    Yang Y, Yang Y, Huang Z, Shen HT, Nie F (2011) Tag localization with spatial correlations and joint group sparsity. In: IEEE conference on computer vision and pattern recognition, pp 881–888. IEEEGoogle Scholar
  28. 28.
    Yang Y, Yang Y, Shen HT (2013) Effective transfer tagging from image to video. ACM Trans Multimedia Comput Commun Appl 9(2):14:1–14:20CrossRefGoogle Scholar
  29. 29.
    Yang Y, Yang Y, Shen HT, Zhang Y, Du X, Zhou X (2013) Discriminative nonnegative spectral clustering with out-of-sample extension. IEEE Trans Knowl Data Eng 25(8):1760–1771CrossRefGoogle Scholar
  30. 30.
    Yang Y, Zha ZJ, Gao Y, Zhu X, Chua TS (2014) Exploiting web images for semantic video indexing via robust sample-specific loss. IEEE Trans Multimedia 16(6):1677–1689CrossRefGoogle Scholar
  31. 31.
    Zhang L, Gao Y, Hong C, Feng Y, Zhu J, Cai D (2013) Feature correlation hypergraph: exploiting high-order potentials for multimodal recognition. IEEE Trans Cybern 44(8):1408–1419CrossRefGoogle Scholar
  32. 32.
    Zhang L, Gao Y, Lu K, Shen J, Ji R (2014) Representative discovery of structure cues for weakly-supervised image segmentation. IEEE Trans Multimedia 16(2):470–479CrossRefGoogle Scholar
  33. 33.
    Zhang L, Gao Y, Xia Y, Dai Q, Li X (2014) A fine-grained image categorization system by cellet-encoded spatial pyramid modeling. IEEE Trans Ind Electron:1Google Scholar
  34. 34.
    Zhang L, Han Y, Yang Y, Song M, Yan S, Tian Q (2013) Discovering discriminative graphlets for aerial image categories recognition. IEEE Trans Image Process 22(12):5071–5084MathSciNetCrossRefGoogle Scholar
  35. 35.
    Zhang L, Song M, Liu X, Bu J, Chen C (2013) Fast multi-view segment graph kernel for object classification. Signal Process 93(6):1597–1607CrossRefGoogle Scholar
  36. 36.
    Zhang L, Song M, Liu X, Sun L, Chen C, Bu J (2014) Recognizing architecture styles by hierarchical sparse coding of blocklets. Inf Sci 254:141–154CrossRefGoogle Scholar
  37. 37.
    Zhang L, Yang M, Feng X (2011) Sparse representation or collaborative representation: which helps face recognition? In: Proc. IEEE int. conf. computer vision, pp 471–478Google Scholar
  38. 38.
    Zhang L, Yang Y, Gao Y, Yu Y, Wang C, Li X (2014) A probabilistic associative model for segmenting weakly-supervised images. IEEE Trans Image Process 23(9):4150–4159MathSciNetCrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  • Fumin Shen
    • 1
  • Wankou Yang
    • 2
  • Hanxi Li
    • 3
    Email author
  • Hanwang Zhang
    • 4
  • Heng Tao Shen
    • 5
  1. 1.School of Computer Science and EngineeringUniversity of Electronic Science and Technology of ChinaChengduChina
  2. 2.School of AutomationSoutheast UniversityNanjingChina
  3. 3.School of Computer and Information EngineeringJiangxi Normal UniversityNanchangChina
  4. 4.School of ComputingNational University of SingaporeSingaporeSingapore
  5. 5.School of Information Technology & Electrical EngineeringThe University of QueenslandBrisbaneAustralia

Personalised recommendations