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An alternative to face image representation and classification

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Abstract

Sparse representation has brought a breakthrough to the face recognition community. It mainly attributes to the creative idea representing the probe face image by a linear combination of the gallery images. However, for face recognition applications, sparse representation still suffers from the following problem: because the face image varies with the illuminations, poses and facial expressions, the difference between the test sample and training samples from the same subject is usually large. Consequently, the representation on the probe face image provided by the original gallery images is not competent in accurately representing the probe face, which may lead to misclassification. In order to overcome this problem, we propose to modify training samples to produce an alternative set of the original training samples, and use both of the original set and produced set to obtain better representation on the test sample. The experimental results show that the proposed method can greatly improve previous sparse representation methods. It is notable that the error rate of classification of the proposed method can be 10% lower than previous sparse representation methods.

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References

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

    Article  Google Scholar 

  2. Zhang Z, Xu Y, Yang J, Li X, Zhang D (2015) A survey of sparse representation: algorithms and applications. IEEE Access 3:490–530

    Article  Google Scholar 

  3. Kroeker K (2009) Face recognition breakthrough. Commun ACM 52:18–19

    Google Scholar 

  4. Qiu Q, Jiang Z, Chellappa R (2011) Sparse dictionary-based representation and recognition of action attributes. In: Proceedings of the international conference on computer vision (ICCV), pp 707–714

  5. Dong W, Li X, Zhang L, Shi G (2011) Sparsity-based image denoising via dictionary learning and structural clustering. In: IEEE conference on computer vision pattern recognition, pp 457–464

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

    Article  MathSciNet  MATH  Google Scholar 

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

  8. Guleryuz O (2006) Nonlinear approximation based image recovery using adaptive sparse reconstructions and iterated denoising-part I: theory. IEEE Trans Image Process 15:539–554

    Article  Google Scholar 

  9. Zhang Z, Shao L, Xu Y, Liu L, Yang J (2017) Marginal representation learning with graph structure self-adaptation. IEEE Trans Neural Netw Learn Syst. https://doi.org/10.1109/TNNLS.2017.2772264

    Google Scholar 

  10. Mei X, Ling H, Jacobs D, Illumination recovery from image with cast shadows via sparse representation. IEEE Trans Image Process 20(2011):2366–2377

  11. Donoho D, Elad M (2003) Optimally sparse representation in general (nonorthogonal) dictionaries via ℓ1 minimization. Proc Natl Acad Sci 100:2197–2202

    MathSciNet  MATH  Google Scholar 

  12. Pati Y, Ramin R, Krishnaprasad P (1993) Orthogonal matching pursuit: recursive function approximation with applications to wavelet decomposition. In: IEEE conference record of the twenty-seventh asilomar conference on signals systems computers, pp 40–44

  13. Xue X, Han H, Wang S, Qin C (2016) Computational experiment-based evaluation on context-aware O2O service recommendation. IEEE Trans Serv Comput. https://doi.org/10.1109/TSC.2016.2638083

    Google Scholar 

  14. 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 59:797–829

    Article  MATH  Google Scholar 

  15. Zhang Z, Lai Z, Xu Y, Shao L, Wu J, Xie G (2017) Discriminative elastic-net regularized linear regression. Accept IEEE Trans Image Process 26(3):1466–1481

    Article  MathSciNet  MATH  Google Scholar 

  16. Liu Q, Lai Z, Zhou Z, Kuang F, Jin Z (2016) A truncated nuclear norm regularization method based on weighted residual error for matrix completion. IEEE Trans Image Process 25:316–330

    Article  MathSciNet  MATH  Google Scholar 

  17. Naseem I, Togneri R, Bennamoun M (2012) Robust regression for face recognition. Pattern Recogn 45:104–118

    Article  Google Scholar 

  18. Fan Z, Ni M, Zhu Q et al (2015) L0-norm sparse representation based on modified genetic algorithm for face recognition. J Vis Commun Image Represent 28:15–20

    Article  Google Scholar 

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

  20. Ren C, Dai D, Yan H (2012) Robust classification using ℓ2,1-norm based regression model. Pattern Recogn 45:2708–2718

    Article  MATH  Google Scholar 

  21. Zhang Z, Xu Y, Shao L, Yang J (2017) Discriminative block-diagonal representational learning for image recognition. IEEE Trans Neural Netw Learn Syst. https://doi.org/10.1109/TNNLS

    Google Scholar 

  22. Lai Z, Wong W, Xu Y, Yang J, Tang J, Zhang D (2016) Approximate orthogonal sparse embedding for dimensionality reduction. IEEE Trans Neural Netw Learn Syst 27:723–735

    Article  MathSciNet  Google Scholar 

  23. Gao S, Tsang I, Chia L (2010) Kernel sparse representation for image classification and face recognition. Comput Vis ECCV 6314:1–14

    Google Scholar 

  24. Yang S, Han Y, Zhang X (2012) A sparse kernel representation method for image classification. In: The 2012 international joint conference on neural networks (IJCNN), pp 1–7

  25. d’Aspremont A, Bach F, Ghaoui L (2008) Optimal solutions for sparse principal component analysis. J Mach Learn Res 9:1269–1294

    MathSciNet  MATH  Google Scholar 

  26. Lai Z, Xu Y, Chen Q, Yang J, Zhang D (2014) Multilinear sparse principal component analysis. IEEE Trans Neural Netw Learn Syst 25:1942–1950

    Article  Google Scholar 

  27. Lai Z, Wong WK, Xu Y, Sun M, Zhao C (2014) Sparse alignment for robust tensor learning. IEEE Trans Neural Netw Learn Syst 25:1779–1792

    Article  Google Scholar 

  28. Amir B, Teboulle M (2009) A fast iterative shrinkage-thresholding algorithm for linear inverse problems. SIAM J Imaging Sci 2:183–202

    Article  MathSciNet  MATH  Google Scholar 

  29. Asif MS, Romberg J (2013) Sparse recovery of streaming signals using l1-homotopy. arXiv:1306.3331

  30. Yang A, Zhou Z, Ganesh A et al (2010) Fast ℓ1-minimization algorithms for robust face recognition. arXiv:1007.3753

  31. Yang A, Sastry S, Ganesh A et al (2010) Fast ℓ1-minimization algorithms and an application in robust face recognition: a review. In: The 17th IEEE international conference on image processing (ICIP), pp 1849–1852

  32. Zhang L, Yang M, Feng X et al (2012) Collaborative representation based classification for face recognition. arXiv:1204.2358

  33. Yang M, Zhang L, Zhang D et al (2012) Relaxed collaborative representation for pattern classification. In: IEEE conference on computer vision and pattern recognition, pp 2224–2231

  34. Zhu Q, Xu Y, Wang J et al (2012) Kernel based sparse representation for face recognition. In: 21st International conference on pattern recognition, pp 1703–1706

  35. Dornaika F, Traboulsi Y, Hernandez C, Assoum A (2013) Self-optimized two phase test sample sparse representation method for image classification. In: The 2nd international conference on advances in biomedical engineering (ICABME), pp 163–166

  36. Dornaika F, Traboulsi Y, Assoum A (2013) Adaptive two phase sparse representation classifier for face recognition. In: Advanced concepts for intelligent vision systems, pp 182–191

  37. Yang J, Luo L, Qian J, Tai Y, Zhang F (2017) Nuclear norm based matrix regression with applications to face recognition with occlusion and illumination changes. IEEE Trans Pattern Anal Mach Intell 39(1):156–171

    Article  Google Scholar 

  38. Qian J, Luo L, Yang J, Zhang F, Lin Z (2015) Robust nuclear norm regularized regression for face recognition with occlusion. Pattern Recogn 48(10):3145–3159

    Article  Google Scholar 

  39. Qian J, Yang J (2013) General regression and representation model for face recognition. In: Biometrics workshop in conjunction with IEEE conference on computer vision and pattern recognition (CVPRW), pp 166–172

  40. Ashfaq RAR, Wang XZ (2017) Impact of fuzziness categorization on divide and conquer strategy for instance selection. J Intell Fuzzy Syst 33(3):1007–1018

    Article  Google Scholar 

  41. Ashfaq RAR, Wang XZ, Huang JZ et al (2017) Fuzziness based semi-supervised learning approach for intrusion detection system. Inf Sci 378:484–497

    Article  Google Scholar 

  42. Wang XZ, Wang R, Feng HM, Wang HC (2014) A new approach to classifier fusion based on upper integral. IEEE Trans Cybern 44(5):620–635

    Article  MathSciNet  Google Scholar 

  43. Lavreniuk MS, Skaku SV, Shelestov AJ et al (2016) Large-scale classification of land cover using retrospective satellite data. Cybern Syst Anal 52:127–138

    Article  MATH  Google Scholar 

  44. Chavez-Garcia RO, Aycard O (2016) Multiple sensor fusion and classification for moving object detection and tracking. IEEE Trans Intell Transp Syst 17:525–534

    Article  Google Scholar 

  45. Guan X, Liu G, Huang C et al (2017) An object-based linear weight assignment fusion scheme to improve classification accuracy using Landsat and MODIS data at the decision level. IEEE Trans Geosci Remote Sens 55(12):6989–7002

    Article  Google Scholar 

  46. Liu H, Yan M, Song E et al (2017) Label fusion method based on sparse patch representation for the brain MRI image segmentation. IET Image Process 11(7):502–511

    Article  Google Scholar 

  47. Tong T, Gray K, Gao Q et al (2017) Multi-modal classification of Alzheimer’s disease using nonlinear graph fusion. Pattern Recogn 63:171–181

    Article  Google Scholar 

  48. Nan Z, Yang J (2010) K Nearest Neighbor based local sparse representation classifiers. In: 2010 Chinese conference on pattern recognition (CCPR), pp 1–5

  49. Ma L, Xiao B, Wang C (2010) Sparse representation based on K-nearest neighbor classifier for degraded Chinese character recognitions. In: Advances in multimedia information processing—PCM 2010, pp 506–514

  50. https://www.nist.gov/programs-projects/face-recognition-technology-feret. Accessed 15 Feb 2017

  51. http://www.cl.cam.ac.uk/research/dtg/attarchive/facedatabase.html. Accessed 15 Feb 2017

  52. http://www.anefian.com/research/face_reco. Accessed 15 Feb 2017

  53. http://vis-www.cs.umass.edu/lfw. Accessed 15 Feb 2017

  54. Kim S, Koh K, Lustig M et al (2007) An interior-point method for large-scale ℓ1-regularized least squares. IEEE J Select Top Signal Process 1:606–617

    Article  Google Scholar 

  55. Liu Z, Pu J, Huang T, Qiu Y (2013) A novel classification method for palmprint recognition based on reconstruction error and normalized distance. Appl Intell 39:407–414

    Google Scholar 

Download references

Acknowledgements

This article is partly supported by National Natural Science Foundation of China (no. 61501230), Natural Science Foundation of Jiangsu Province (no. BK20150751), China Post-doctoral Science Foundation funded project (no. 2015M570446), Jiangsu Planned Projects for Postdoctoral Research Funds (no. 1402047B), and Open Fund Project of Fujian Provincial Key Laboratory of Information Processing and Intelligent Control (Minjiang University) (no. MJUKF201726).

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Authors and Affiliations

  1. College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, 210016, China

    Qi Zhu, Ning Yuan, Donghai Guan, Nuoya Xu & Huijie Li

  2. Fujian Provincial Key Laboratory of Information Processing and Intelligent Control, Minjiang University, Fuzhou, 350121, China

    Qi Zhu

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  1. Qi Zhu
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  2. Ning Yuan
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  3. Donghai Guan
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  4. Nuoya Xu
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  5. Huijie Li
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Correspondence to Donghai Guan.

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Zhu, Q., Yuan, N., Guan, D. et al. An alternative to face image representation and classification. Int. J. Mach. Learn. & Cyber. 10, 1581–1589 (2019). https://doi.org/10.1007/s13042-018-0802-0

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