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Neural Computing and Applications

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01-09-2014 | Original Article

Combination of linear regression classification and collaborative representation classification

Authors: Hongzhi Zhang, Faqiang Wang, Yan Chen, Dapeng Zhang, Kuanquan Wang, Jingdong Liu

Published in: Neural Computing and Applications | Issue 3-4/2014

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Abstract

Classification using the l ₂-norm-based representation is usually computationally efficient and is able to obtain high accuracy in the recognition of faces. Among l ₂-norm-based representation methods, linear regression classification (LRC) and collaborative representation classification (CRC) have been widely used. LRC and CRC produce residuals in very different ways, but they both use residuals to perform classification. Therefore, by combining the residuals of these two methods, better performance for face recognition can be achieved. In this paper, a simple weighted sum based fusion scheme is proposed to integrate LRC and CRC for more accurate recognition of faces. The rationale of the proposed method is analyzed. Face recognition experiments illustrate that the proposed method outperforms LRC and CRC.

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

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

Zhao W, Chellappa R, Stublers PO (2006) Face processing: advanced modeling and methods. J Electron Imaging 15(4):049901–049901-1CrossRef

Chang K, Bowyer KW, Sarkar S, Victor B (2003) Comparison and combination of ear and face images in appearance-based biometrics. IEEE Trans Pattern Anal Mach Intell 25(9):1160–1165CrossRef

Ren Ch-X, Dai D-Q (2010) Incremental learning of bidirectional principal components for face recognition. Pattern Recognit 43(1):318–330MATHCrossRef

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 24(1):131–137CrossRef

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

Chen H-T, Chang H-W, Liu T-L (2005) Local discriminant embedding and its variants. In: Proceedings of the IEEE computer society conference on computer vision pattern recognition, pp 846–853

Wang J, Xu Y, Zhang D, You J (2010) An efficient method for computing orthogonal discriminant vectors. Neurocomputing 73(10):2168–2176CrossRef

10.

Yang J, Zhang D, Xu Y, Yang J-Y (2005) Two-dimensional discriminant transform for face recognition. Pattern Recognit 38(7):1125–1129MATHCrossRef

11.

Xu Y, Zhang D (2010) Represent and fuse bimodal biometric images at the feature level: complex-matrix-based fusion scheme. Opt Eng 49(3):037002–037002-6CrossRef

12.

Yang J, Yang J-Y, Frangi AF (2003) Combined fisherfaces framework. Image Vision Comput 21(12):1037–1044CrossRef

13.

Zhang B, Qiao Y (2010) Face recognition based on gradient Gabor feature and efficient kernel fisher analysis. Neural Comput Appl 19(4):617–623MathSciNetCrossRef

14.

Li J, Pan J, Lu Z (2009) Face recognition using Gabor-based complete kernel fisher discriminant analysis with fractional power polynomial models. Neural Comput Appl 18(6):613–621CrossRef

15.

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

16.

Wright J, Yang AY, Ganesh A et al (2009) Robust face recognition via sparse representation. IEEE Trans Pattern Anal Mach Intell 31(2):210–227CrossRef

17.

Wagner A, Wright J, Ganesh A, Zhou ZH, Ma Y (2009) Towards a practical face recognition system: robust registration and illumination by sparse representation. In: IEEE computer society conference on computer vision pattern recognition workshops, pp 597–604

18.

Yang M, Zhang L (2010) Gabor feature based sparse representation for face recognition with Gabor occlusion dictionary. In: Proceedings of the 11th European conference on computer vision, pp 448–461

19.

Zhang H, Nasrabadi NM, Zhang Y, Huang TS (2012) Joint dynamic sparse representation for multi-view face recognition. Pattern Recognit 45(4):1290–1298CrossRef

20.

Bach F, Mairal J, Ponce J, Sapiro G (2010) Sparse coding and dictionary learning for image analysis. CVPR’10 Tutorial, San Francisco

21.

Candes E, Tao T (2007) The Dantzig selector: statistical estimation when p is much larger than n. Ann Stat 35(6):2313–2351MATHMathSciNetCrossRef

22.

Yang J, Zhang L, Xu Y, Yang J-Y (2012) Beyond sparsity: the role of L1-optimizer in pattern classification. Pattern Recognit 45(3):1104–1118MATHCrossRef

23.

Wright J, Ma Y, Mairal J, Sapiro G, Huang T, Yan S (2009) Sparse representation for computer vision and pattern recognition. Proc IEEE 98(6):1031–1044CrossRef

24.

Donoho D (2006) For most large underdetermined systems of linear equations the minimal l1-norm solution is also the sparsest solution. Commun Pure Appl Math 56(6):797–829MathSciNetCrossRef

25.

Shi Q, Eriksson A, Hengel A, Shen C (2011) Is face recognition really a compressive sensing problem? In: Proceedings of the IEEE computer society conference on computer vision pattern recognition, pp 553–560

26.

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):1255–1262CrossRef

27.

Zuo W, Meng D, Zhang L, Feng X, Zhan DG (2013) A generalized iterated shrinkage algorithm for non-convex sparse coding. In: Proceedings of the IEEE international conference on computer vision, pp 217–224

28.

Naseem I, Togneri R, Bennamoun M (2010) Linear regression for face recognition. IEEE Trans Pattern Anal Mach Intell 32(11):2106–2112CrossRef

29.

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

30.

Xu Y, Zhu Q, Zhang D, Yang J-Y (2011) Combine crossing matching scores with conventional matching scores for bimodal biometrics and face and palmprint recognition experiments. Neurocomputing 74:3946–3952CrossRef

31.

Xu Y, Zhong A, Yang J, Zhang D (2011) Bimodal biometrics based on a representation and recognition approach. Opt Eng 50(3):037202–037202-7CrossRef

32.

Zhang L, Yang M, Feng Z, Zhang D (2010) On the dimensionality reduction for sparse representation based face recognition. In: Proceedings of the international conference on pattern recognition, pp 1237–1240

33.

Xu Y, Zuo W, Fan Z (2011) Supervised sparse presentation method with a heuristic strategy and face recognition experiments. Neurocomputing 79:125–131CrossRef

34.

Zhang S, Gu X (2012) Palmprint recognition method based on score level fusion. Optik Int J Light Electron Opt 124(18):3340–3344

35.

Yang J, Wright J, Huang TS, Ma Y (2010) Image super-resolution via. sparse representation. IEEE Trans Image Process 19(11):2861–2873MathSciNetCrossRef

36.

Dong W, Shi G, Zhang L, Wu X (2010) Super-resolution with nonlocal regularized sparse representation. In: Proceedings of the SPIE international society optical engineering, pp 1899–2013

37.

Yang J, Wright J, Huang T, Ma Y (2008) Image super-resolution as sparse representation of raw image patches. In: IEEE conference computer vision pattern recognition, pp 1–8

38.

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

39.

Dong W, Zhang L, Shi G, Li X (2013) Nonlocally centralized sparse representation for image restoration. IEEE Trans Image Process 22(4):1620–1630MathSciNetCrossRef

40.

Yang AY, Wright J, Ma Y, Sastry S (2007) Feature selection in face recognition: a sparse representation perspective. Technical report, UC Berkeley (UCB)

41.

http://www.cl.cam.ac.uk/research/dtg/attarchive/facedatabase.html

42.

http://www2.ece.ohio-state.edu/~aleix/ARdatabase.html

43.

Martinez AM, Benavente R (1998) The AR face database. CVC technical report #24

Title: Combination of linear regression classification and collaborative representation classification
Authors: Hongzhi Zhang
Faqiang Wang
Yan Chen
Dapeng Zhang
Kuanquan Wang
Jingdong Liu
Publication date: 01-09-2014
Publisher: Springer London
Published in: Neural Computing and Applications / Issue 3-4/2014
Print ISSN: 0941-0643
Electronic ISSN: 1433-3058
DOI: https://doi.org/10.1007/s00521-014-1564-6

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