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Robust Face Hallucination via Locality-Constrained Nuclear Norm Regularized Regression

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Intelligence Science and Big Data Engineering (IScIDE 2017)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 10559))

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Abstract

The performance of traditional face recognition approaches is sharply reduced when facing a low-resolution (LR) probe face image. Various face hallucination methods have been proposed in the past decade to obtain much more facial details. The basic idea of face hallucination is to desire a high-resolution (HR) face image from an observed LR one with the help of a set of training examples. In this paper, we propose a locality-constrained nuclear norm regularized regression (LCNNR) model for face hallucination task and use the alternating direction method of multipliers to solve it. LCNNR attempts to directly use the image matrix to compute the representation coefficients to maintain the essential structural information. Moreover, a locality constraint is also enforced to preserve the locality and the sparsity simultaneously. Experiments carried out on the benchmark FEI face database show that LCNNR outperforms some state-of-the-art algorithms.

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References

  1. Jain, A.K., Ross, A., Prabhakar, S.: An introduction to biometric recognition. IEEE Trans. Circuits Syst. Video Technol. 14(1), 4–20 (2004)

    Article  Google Scholar 

  2. Gao, G., Yang, J., Wu, S., Jing, X., Yue, D.: Bayesian sample steered discriminative regression for biometric image classification. Appl. Soft Comput. 37, 48–59 (2015)

    Article  Google Scholar 

  3. Jing, X., Wu, F., Zhu, X., Dong, X., Ma, F., Li, Z.: Nulti-spectral low-rank structured dictionary learning for face recognition. Pattern Recogn. 59, 14–25 (2016)

    Article  Google Scholar 

  4. Baker, S., Kanade, T.: Limits on super-resolution and how to break them. IEEE Trans. Pattern Anal. Mach. Intell. 24(9), 1167–1183 (2002)

    Article  Google Scholar 

  5. Wang, X., Tang, X.: Hallucinating face by eigen transformation. IEEE Trans. Syst. Man Cybern. Part C Appl. Rev. 35(3), 425–434 (2005)

    Article  Google Scholar 

  6. An, L., Bhanu, B.: Face image super-resolution using 2D CCA. Sig. Process. 103, 184–194 (2014)

    Article  Google Scholar 

  7. Chang, H., Yeung, D.-Y., Xiong, Y.: Super-resolution through neighbor embedding. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1275–1282 (2004)

    Google Scholar 

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

    Article  MathSciNet  Google Scholar 

  9. Gao, G., Yang, J.: A novel sparse representation based framework for face image super-resolution. Neurocomputing 134, 92–99 (2014)

    Article  Google Scholar 

  10. Ma, X., Zhang, J., Qi, C.: Hallucinating face by position-patch. Pattern Recogn. 43(6), 2224–2236 (2010)

    Article  Google Scholar 

  11. Jung, C., Jiao, L., Liu, B., Gong, M.: Position-patch based face hallucination using convex optimization. IEEE Sig. Process. Lett. 18(6), 367–370 (2011)

    Article  Google Scholar 

  12. Wang, Z., Hu, R., Wang, S., Jiang, J.: Face hallucination via weighted adaptive sparse regularization. IEEE Trans. Circuits Syst. Video Technol. 24(5), 802–813 (2014)

    Article  Google Scholar 

  13. Jiang, J., Hu, R., Wang, Z., Han, Z.: Noise robust face hallucination via locality-constrained representation. IEEE Trans. Multimedia 16(5), 1268–1281 (2014)

    Article  Google Scholar 

  14. Yang, J., Luo, L., Qian, J., Tai, Y., Zhang, F., Xu, Y.: 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 (2017)

    Article  Google Scholar 

  15. Gao, G., Yang, J., Jing, X.-Y., Shen, F., Yang, W., Yue, D.: Learning robust and discriminative low-rank representations for face recognition with occlusion. Pattern Recogn. 66, 129–143 (2017)

    Article  Google Scholar 

  16. Cai, J.F., Candes, E.J., Shen, Z.W.: A singular value thresholding algorithm for matrix completion. SIAM J. Optim. 20(4), 1956–1982 (2010)

    Article  MathSciNet  MATH  Google Scholar 

  17. Thomaz, C.E., Giraldi, G.A.: A new ranking method for principal components analysis and its application to face image analysis. Image Vis. Comput. 28(6), 902–913 (2010)

    Article  Google Scholar 

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Authors and Affiliations

  1. Institute of Advanced Technology, Nanjing University of Posts and Telecommunications, Nanjing, People’s Republic of China

    Guangwei Gao & Dong Yue

  2. Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education, Nanjing University of Science and Technology, Nanjing, People’s Republic of China

    Guangwei Gao & Jian Yang

  3. School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, People’s Republic of China

    Jian Yang

  4. Jiangsu Key Laboratory of Big Data Security and Intelligent Processing, Nanjing University of Posts and Telecommunications, Nanjing, People’s Republic of China

    Pu Huang

  5. Fujian Provincial Key Laboratory of Information Processing and Intelligent Control, Minjiang University, Fuzhou, People’s Republic of China

    Guangwei Gao & Zuoyong Li

Authors
  1. Guangwei Gao
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  2. Jian Yang
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  3. Pu Huang
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  4. Zuoyong Li
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  5. Dong Yue
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Corresponding author

Correspondence to Guangwei Gao .

Editor information

Editors and Affiliations

  1. Dalian University of Technology, Dalian, China

    Yi Sun

  2. Dalian University of Technology, Dalian, China

    Huchuan Lu

  3. Dalian University of Technology, Dalian, China

    Lihe Zhang

  4. Nanjing University of Science and Technology, Nanjing, China

    Jian Yang

  5. Beijing Institute of Technology, Beijing, China

    Hua Huang

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Gao, G., Yang, J., Huang, P., Li, Z., Yue, D. (2017). Robust Face Hallucination via Locality-Constrained Nuclear Norm Regularized Regression. In: Sun, Y., Lu, H., Zhang, L., Yang, J., Huang, H. (eds) Intelligence Science and Big Data Engineering. IScIDE 2017. Lecture Notes in Computer Science(), vol 10559. Springer, Cham. https://doi.org/10.1007/978-3-319-67777-4_22

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  • DOI: https://doi.org/10.1007/978-3-319-67777-4_22

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-67776-7

  • Online ISBN: 978-3-319-67777-4

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