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Low-resolution degradation face recognition over long distance based on CCA

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Abstract

Canonical correlation analysis (CCA) is a kind of classical multivariate analysis method. Less canonical correlation variables are used to describe the relationship between two variables completely but easily. To get high face recognition rate under low-resolution degradation over a long distance solidly, in this work, CCA is used to extract the correlation between high-resolution face images and low-resolution ones and to find the transform pair between them. Therefore, face images of the same individual with variable resolutions can be matched accurately. This is the first method that uses CCA to do low-resolution degradation face recognition over long distances. We conduct the experiments on the Extended Yale B and ORL database, and the experimental results validate the efficacy of the proposed method.

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References

  1. Zhao W, Chellappa R, Rosenfel A, Phillips P (2003) Face recognition: a literature survey. ACM Comput Surv 35(4):399–458

  2. Belhumeur V, Hespanha J, Kriegman D (1997) Eigen faces vs Fisher faces: recognition using class specific linear projection. IEEE Trans Pattern Anal Mach Intell 19(7):711–720

    Article  Google Scholar 

  3. Fukunaga K (1990) Introduction to statistical pattern recognition, 2nd edn. Academic Press, Boston

    MATH  Google Scholar 

  4. He X, Yan S, Hu Y, Niyogi P, Zhang H (2005) Face recognition using Laplacian faces. IEEE Trans Pattern Anal Mach Intell 27(3):328–340

    Article  Google Scholar 

  5. Yang J, Zhang D, Yang J, Niu B (2007) Globally maximizing, locally minimizing: unsupervised discriminant projection with applications to face and palm biometrics. IEEE Trans Pattern Anal Mach Intell 29(4):650–664

    Article  Google Scholar 

  6. Lai Z, Xu Y, Yang J, Tang J, Zhang D (2013) Sparse tensor discriminant analysis. IEEE Trans Image Process 22(10):3904–3915

    Article  MathSciNet  Google Scholar 

  7. Lai Z, Jin J, Yang J, Sun M (2011) Locality preserving embedding for face and handwriting digital recognition. Neural Comput Appl 20(4):565–573

    Article  Google Scholar 

  8. Lai Z, Jin Z, Yang J, Sun M (2012) Dynamic transition embedding for image feature extraction and recognition. Neural Comput Appl 21(8):1905–1915

    Article  Google Scholar 

  9. Zhao G, Miao D, Lai Z, Gao C, Liu C, Yang J (2013) Two-dimensional color uncorrelated discriminant analysis for face recognition. Neurocomputing 113(3):251–261

    Article  Google Scholar 

  10. Zhao C, Lai Z, Liu C, Gu X, Qian J (2012) Fuzzy local maximal marginal embedding for feature extraction. Soft Comput 16(1):77–87

    Article  Google Scholar 

  11. Raudys SJ, Jain AK (1991) Small sample size effects in statistical pattern recognition: recommendations for practitioners. IEEE Trans Patt Anal Mach Intell 13(3):252–264

    Article  Google Scholar 

  12. Hong Z, Yang J (1991) Optimal discriminant plane for a small number of samples and design method of classifier on the plane. Pattern Recognit 24(4):317–324

    Article  MathSciNet  Google Scholar 

  13. Friedman JH (1989) Regularized discriminant analysis. J Am Stat Assoc 84:165–175

    Article  Google Scholar 

  14. Hastie T, Tibshirani R (1995) Penalized discriminant analysis. Ann Stat 23:73–102

    Article  MathSciNet  MATH  Google Scholar 

  15. Hastie T, Tibshirani R, Buja A (1994) Flexible discriminant analysis by optimal scoring. J Am Stat Assoc 89:1255–1270

    Article  MathSciNet  MATH  Google Scholar 

  16. Chen LF, Liao HYM, Ko MT, Yu GJ (2000) A new LDA-based face recognition system which can solve the small sample size problem. Pattern Recognit 33(1):1713–1726

    Article  Google Scholar 

  17. Yu H, Yang J (2001) A direct LDA algorithm for high dimensional data—with application to face recognition. Pattern Recognit 34(10):2067–2070

    Article  MATH  Google Scholar 

  18. Loog M (2001) Approximate pairwise accuracy criterion for multiclass linear dimension reduction: generalization of the Fisher criterion. IEEE Trans Pattern Anal Mach Intell 26(7):762–766

    Article  Google Scholar 

  19. Tao D, Li X, Wu X, Maybank SJ (2009) Geometric mean for subspace selection. IEEE Trans Pattern Anal Mach Intell 31(2):260–274

    Article  Google Scholar 

  20. Hardoon DR, Szedmak S, Shawe-Taylor J (2004) Canonical correlation analysis: an overview with application to learning methods. Neural Comput 16:2639–2664

    Article  MATH  Google Scholar 

  21. Huang H, He H, Fan X, Zhang J (2010) Super-resolution of human face image using canonical correlation analysis. Pattern Recognit 43:2532–2543

    Article  MATH  Google Scholar 

  22. Martinez AM, Benavente R The AR face database. http://cobweb.ecn.purdue.edu/~aleix/aleix_face_DB.html

  23. Martinez AM, Benavente R (1998) The AR face database. CVC Tech Rep 24

Download references

Acknowledgments

This work was supported in part by the National Natural Science Foundation of China under Grant Nos. 61201370, 61375001 and supported by the open fund of Key Laboratory of Measurement and Control of Complex Systems of Engineering, Ministry of Education (No. MCCSE2013B01).

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Authors and Affiliations

  1. School of Automation, Southeast University, Nanjing, 210096, China

    Zhenyu Wang & Wankou Yang

  2. Key Laboratory of Measurement and Control of Complex Systems of Engineering, Ministry of Education, Southeast University, Nanjing, 210096, China

    Zhenyu Wang & Wankou Yang

  3. School of Information Science and Engineering, Shandong University, Jinan, 250100, China

    Xianye Ben

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  1. Zhenyu Wang
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  2. Wankou Yang
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  3. Xianye Ben
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Correspondence to Wankou Yang.

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Wang, Z., Yang, W. & Ben, X. Low-resolution degradation face recognition over long distance based on CCA. Neural Comput & Applic 26, 1645–1652 (2015). https://doi.org/10.1007/s00521-015-1834-y

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  • DOI: https://doi.org/10.1007/s00521-015-1834-y

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