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International Workshop on Multi-disciplinary Trends in Artificial Intelligence

MIWAI 2015: Multi-disciplinary Trends in Artificial Intelligence pp 171–183Cite as

Integrating Simplified Inverse Representation and CRC for Face Recognition

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9426))

Abstract

The representation based classification method (RBCM) has attracted much attention in the last decade. RBCM exploits the linear combination of training samples to represent the test sample, which is then classified according to the minimum reconstruction residual. Recently, an interesting concept, Inverse Representation (IR), is proposed. It is the inverse process of conventional RBCMs. IR applies test samples’ information to represent each training sample, and then classifies the training sample as a useful supplement for the final classification. The relative algorithm CIRLRC, integrating IR and linear regression classification (LRC) by score fusing, shows superior classification performance. However, there are two main drawbacks in CIRLRC. First, the IR in CIRLRC is not pure, because the test vector contains some training sample information. The other is the computation inefficiency because CIRLRC should solve C linear equations for classifying the test sample respectively, where C is the number of the classes. Therefore, we present a novel method integrating simplified IR (SIR) and collaborative representation classification (CRC), named SIRCRC, for face recognition. In SIRCRC, only test sample information is fully used in SIR, and CRC is more efficient than LRC in terms of speed, thus, one linear equation system is needed. Extensive experimental results on face databases show that it is very competitive with both CIRLRC and the state-of-the-art RBCM.

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References

  1. Li, Y., Zhu, L., Bi, N., Xu, Y.: Sparse representation for brain signal processing: a tutorial on methods and applications. Sig. Process. 31(3), 96–106 (2014)

    Article  Google Scholar 

  2. Boufounos, P., Kutyniok, G., Rauhut, H.: Sparse recovery from combined fusion frame measurements. IEEE Trans. Inf. Theor. 57(6), 3864–3876 (2011)

    Article  MathSciNet  Google Scholar 

  3. Arbelaez, P., Maire, M., Fowlkes, C., Malik, J.: Contour detection and hierarchical image segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 33(5), 898–916 (2011)

    Article  Google Scholar 

  4. Yang, J., Wright, J., Huang, T.S., Ma, Y.: Image super-resolution via sparse representation. IEEE Trans. Image Proc. 19(11), 2861–2873 (2010)

    Article  MathSciNet  Google Scholar 

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

    Article  MathSciNet  Google Scholar 

  6. Mei, X., Ling, H.: Robust visual tracking and vehicle classification via sparse representation. IEEE Trans. Pattern Anal. Mach. Intell. 33(11), 2259–2272 (2011)

    Article  Google Scholar 

  7. Jia, X., Lu, H., Yang, M.-H.: Visual tracking via adaptive structural local sparse appearance model. In: IEEE Conference on Computer vision and pattern recognition (CVPR), pp. 1822–1829 (2012)

    Google Scholar 

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

    Article  Google Scholar 

  9. Zhang, D., Yang, M., Feng, X.: Sparse representation or collaborative representation: which helps face recognition?. In: IEEE International Conference on Computer Vision (ICCV), pp. 471–478 (2011)

    Google Scholar 

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

    Article  Google Scholar 

  11. Yang, M., Zhang, D., Shenlong, W.: Relaxed collaborative representation for pattern classification. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2224–2231 (2012)

    Google Scholar 

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

    Article  Google Scholar 

  13. Xu, Y., Fan, Z., Qiu, M., Zhang, D., Yang, J.-Y.: A sparse representation method of bimodal biometrics and palmprint recognition experiments. Neurocomputing 103, 164–171 (2013)

    Article  Google Scholar 

  14. Naseem, I., Togneri, R., Bennamoun, M.: Sparse representation for ear biometrics. In: Bebis, G., et al. (eds.) ISVC 2008, Part II. LNCS, vol. 5359, pp. 336–345. Springer, Heidelberg (2008)

    Chapter  Google Scholar 

  15. Shekhar, S., Patel, V.M., Nasrabadi, N.M., Chellappa, R.: Joint sparse representation for robust multimodal biometrics recognition. IEEE Trans. Pattern Anal. Mach. Intell. 36(1), 113–126 (2014)

    Article  Google Scholar 

  16. Pillai, J.K., Patel, V.M., Chellappa, R., Ratha, N.K.: Secure and robust iris recognition using random projections and sparse representations. IEEE Trans. Pattern Anal. Mach. Intell. 33(9), 1877–1893 (2011)

    Article  Google Scholar 

  17. Yang, J., Yu, K., Gong, Y., Huang, T.: Linear spatial pyramid matching using sparse coding for image classification. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR, pp. 1794–1801 (2009)

    Google Scholar 

  18. Yang, M., Zhang, D., Yang, J.: Robust sparse coding for face recognition. In: Computer Vision and Pattern Recognition (CVPR), pp. 625–632 (2011)

    Google Scholar 

  19. Lyu, Q., Lin, Z., She, Y., Zhang, C.: A comparison of typical l p minimization algorithms. Neurocomputing 119, 413–424 (2013)

    Article  Google Scholar 

  20. Xu, Z., Chang, X., Fengmin, X., Zhang, H.: l 1/2 regularization: a thresholding representation theory and a fast solver. IEEE Trans. Neural Networks Learning Syst. 23(7), 1013–1027 (2012)

    Article  Google Scholar 

  21. Guo, S., Wang, Z., Ruan, Q.: Enhancing sparsity via l p minimization for robust face recognition. Neurocomputing 99, 592–602 (2013)

    Article  Google Scholar 

  22. Zhang, Z., Xu, Y., Yang, J., Li, X., Zhang, D.: A survey of sparse representation: algorithms and applications. doi:10.1109/ACCESS.2015.2430359

  23. Xu, Y., Li, X., Yang, J., Lai, Z., Zhang, D.: Integrating conventional and inverse representation for face recognition. IEEE Trans. Cybernetics 44(10), 1738–1746 (2014)

    Article  Google Scholar 

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

  25. Available: http://www.itl.nist.gov/iad/humanid/feret

  26. Available: http://www.anefian.com/research/face_reco.html

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Acknowledgments

This work is supported in part by the Priority Academic Program Development of Jiangsu Higher Education Institutions, Natural Science Foundation of China (No. 61572258, No. 61103141 and No. 51405241) and Student Innovation Training Program of NUIST(No. 201410300190 and No. 201410300178).

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Authors and Affiliations

  1. Jiangsu Engineering Center of Network Monitoring, Nanjing University of Information Science & Technology, Nanjing, 210024, China

    Yingnan Zhao, Beijing Chen & Xiaoping Zhao

  2. School of Computer & Software, Nanjing University of Information Science & Technology, Nanjing, 210044, China

    Yingnan Zhao, Beijing Chen & Xiaoping Zhao

  3. School of Computing and Communications, University of Technology Sydney, Sydney, Australia

    Yingnan Zhao & Xiangjian He

Authors
  1. Yingnan Zhao
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  2. Xiangjian He
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  3. Beijing Chen
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  4. Xiaoping Zhao
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Corresponding author

Correspondence to Yingnan Zhao .

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Editors and Affiliations

  1. University College of London, London, United Kingdom

    Antonis Bikakis

  2. Fuzhou University, Fuzhou, China

    Xianghan Zheng

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© 2015 Springer International Publishing Switzerland

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Zhao, Y., He, X., Chen, B., Zhao, X. (2015). Integrating Simplified Inverse Representation and CRC for Face Recognition. In: Bikakis, A., Zheng, X. (eds) Multi-disciplinary Trends in Artificial Intelligence. MIWAI 2015. Lecture Notes in Computer Science(), vol 9426. Springer, Cham. https://doi.org/10.1007/978-3-319-26181-2_16

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  • DOI: https://doi.org/10.1007/978-3-319-26181-2_16

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-26180-5

  • Online ISBN: 978-3-319-26181-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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