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Weighted-PCANet for Face Recognition

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Neural Information Processing (ICONIP 2015)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 9492))

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Abstract

Weighted-PCANet, a novel feature learning method is proposed to face recognition by combining Linear Regression Classification model (LRC) and PCANet construction. The sample specific hat matrix is used to handle different images in feature extraction stage. After appropriate adaption, the performance of this new model outperform than various mainstream methods including PCANet for face recognition on Extended YaleB dataset. Particularly, various experiments testify the robustness of weighted-PCANet while dealing with less training samples or corrupted data.

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References

  1. Ahonen, T., Hadid, A., Pietikainen, M.: Face description with local binary patterns: application to face recognition. IEEE Trans. Pattern Anal. Mach. Intell. 28(12), 2037–2041 (2006)

    Article  MATH  Google Scholar 

  2. Chan, T.H., Jia, K., Gao, S., Lu, J., Zeng, Z., Ma, Y.: PCANet: a simple deep learning baseline for image classification? arXiv preprint (2014). arXiv:1404.3606

  3. Chang, C.-C., Lin, C.-J.: LIBSVM: a library for support vector machines. ACM Trans. Intell. Syst. Technol. (TIST) 2(3), 27 (2011)

    Google Scholar 

  4. Deng, W., Hu, J., Guo, J.: In defense of sparsity based face recognition. In: 2013 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 399–406. IEEE (2013)

    Google Scholar 

  5. Georghiades, A.S., Belhumeur, P.N., Kriegman, D.J.: 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 (2001)

    Article  Google Scholar 

  6. Hinton, G.E., Srivastava, N., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.R.: Improving neural networks by preventing co-adaptation of feature detectors. arXiv preprint (2012). arXiv:1207.0580

  7. Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems, pp. 1097–1105 (2012)

    Google Scholar 

  8. Lawrence, S., Giles, C.L., Tsoi, A.C., Back, A.D.: Face recognition: a convolutional neural-network approach. IEEE Trans. Neural Netw. 8(1), 98–113 (1997)

    Article  Google Scholar 

  9. LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proc. IEEE 86(11), 2278–2324 (1998)

    Article  Google Scholar 

  10. 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 

  11. Ngiam, J., Chen, Z., Chia, D., Koh, P.W., Le, Q.V., Ng, A.Y.: Tiled convolutional neural networks. In: Advances in Neural Information Processing Systems (2010)

    Google Scholar 

  12. Saxe, A., Koh, P.W., Chen, Z., Bhand, M., Suresh, B., Ng, A.Y.: On random weights and unsupervised feature learning. In: Proceedings of the 28th International Conference on Machine Learning (ICML-11), pp. 1089–1096 (2011)

    Google Scholar 

  13. Suykens, J.A.K., Vandewalle, J.: Least squares support vector machine classifiers. Neural Process. Lett. 9(3), 293–300 (1999)

    Article  MathSciNet  MATH  Google Scholar 

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

    Article  Google Scholar 

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

    Article  Google Scholar 

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

    Google Scholar 

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

    Google Scholar 

  18. Zhu, Z., Luo, P., Wang, X., Tang, X.: Deep learning identity-preserving face space. In: 2013 IEEE International Conference on Computer Vision (ICCV), pp. 113–120. IEEE (2013)

    Google Scholar 

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Author information

Authors and Affiliations

  1. Graduate School at Shenzhen, Tsinghua University, Shenzhen, 518055, Guangdong, China

    Jiawen Huang & Chun Yuan

Authors
  1. Jiawen Huang
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  2. Chun Yuan
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Corresponding author

Correspondence to Jiawen Huang .

Editor information

Editors and Affiliations

  1. University of Istanbul, Istanbul, Turkey

    Sabri Arik

  2. University at Qatar, Doha, Qatar

    Tingwen Huang

  3. Tunku Abdul Rahman University College, Kuala Lumpur, Malaysia

    Weng Kin Lai

  4. University of Science Technology, Wuhan, China

    Qingshan Liu

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Huang, J., Yuan, C. (2015). Weighted-PCANet for Face Recognition. In: Arik, S., Huang, T., Lai, W., Liu, Q. (eds) Neural Information Processing. ICONIP 2015. Lecture Notes in Computer Science(), vol 9492. Springer, Cham. https://doi.org/10.1007/978-3-319-26561-2_30

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  • DOI: https://doi.org/10.1007/978-3-319-26561-2_30

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-26560-5

  • Online ISBN: 978-3-319-26561-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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