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Adaptive illumination normalization via adaptive illumination preprocessing and modified weber-face

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Abstract

Illumination processing is a challenging task in face recognition. This paper proposes a novel illumination normalization method that aims to remove illumination boundaries and improve image quality under dark conditions. Firstly, to improve the image quality, an adaptive illumination preprocessing algorithm is adopted. Then we modify the Weber-Face model by suppressing the components which are greatly affected by the illumination. Experimental results on both Extended Yale B and CMU-PIE databases show that the proposed method can obtain high performance under complex illumination conditions. The accuracy on the Extended Yale B database is 93.02% and on the CMU-PIE database is 70.44%, which is the highest among the similar approaches. This method not only greatly improves the face recognition rate but also keep the computational complexity in low compared with several state-of-the-art methods.

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References

  1. Han H, Shan S, Chen X et al (2013) A comparative study on illumination preprocessing in face recognition. Pattern Recognit 46(6):1691–1699

    Article  Google Scholar 

  2. Pizer SM, Amburn EP, Austin JD et al (1987) Adaptive histogram equalization and its variations. Comput Vis Graph Image Process 39(3):355–368

    Article  Google Scholar 

  3. Du S, Ward RK (2010) Adaptive region-based image enhancement method for ro- bust face recognition under variable illumination conditions. IEEE Trans Cir- cuits Syst Video Technol 20(9):1165–1175

    Article  Google Scholar 

  4. Savvides M, Kumar BVKV (2003) Illumination normalization using logarithm transforms for face authentication. In: Audio-and Video-Based Biometric Person Authentication. Springer, Berlin Heidelberg, pp 549–556

  5. Shan S, Gao W, Cao B et al (2003) Illumination normalization for robust face recognition against varying lighting conditions. In: IEEE international workshop on analysis and modeling of faces and gestures, 2003. AMFG 2003. IEEE, pp 157–164

  6. Zhao W, Chellappa R (2001) Symmetric shape-from-shading using self-ratio image. Int J Comput Vis 45:55–75

    Article  MATH  Google Scholar 

  7. Xie X, Lam KM (2005) Face recognition under varying illumination based on a 2D Face shape model. Pattern Recognit 38:221–230

    Article  Google Scholar 

  8. Kirby M, Sirovich L (1990) Application of the KL procedure for the characterization of human faces. IEEE Trans Pattern Anal Mach Intell 12(1):103–108

    Article  Google Scholar 

  9. Rujirakul K, Soin C, Arnonkijpanich B (2014) Pem-pca: A parallel expectation maximization pca face recognition architecture. Sci World J 2014(5):468176

    Google Scholar 

  10. Ojala T, Pietikainen M, Harwood DA (1996) Comparative study of texture measures with classification based on feature distributions[J]. IEEE Trans Pattern Recogn 29(1):51–59

    Article  Google Scholar 

  11. Jiang Y, Shen Y, Liu Y, Liu W (2015) Multiclass adaboost elm and its application in lbp based face recognition. Math Probl Eng 2015(2):1–9

    Google Scholar 

  12. Liu L, Fieguth P, Zhao G, Pietikinen M, Hu D (2016) Extended local binary patterns for face recognition. Inf Sci 358-359(C):56–72

    Article  Google Scholar 

  13. Kim M (2015) Sparse discriminative region selection algorithm for face recognition. Appl Intell 42:817

    Article  Google Scholar 

  14. Land EH (1986) Recent advances in retinex theory. Vis Res 26(1):7–21

    Article  Google Scholar 

  15. Jain A K (1989) Fundamentals of digital signal processing. Prentice-Hall, Englewood Cliffs

    MATH  Google Scholar 

  16. Horn BKP (1997) Robot vision. MIT Press, Cambridge

    Google Scholar 

  17. Rahman Z, Jobson DJ, Woodell GA (1996) Multi-scale retinex for color image enhancement. In: International conference on image processing 1996. Proceedings. IEEE, 3, pp 1003–1006

  18. Wang H, Li ZS, Wang Y et al (2004) Self-quotient image for face recognition. In: 2004 international conference on image processing, 2004. ICIP’04. IEEE, 2, pp 1397–1400

  19. Jung C, Yin J (2016) SQI-based illumination normalization for face recognition based on discrete wavelet transform, IEEE ICIP 1664–1668

  20. Chen J, Shan S, He C et al (2010) WLD: a robust local image descriptor. Pattern Anal Mach Intell IEEE Trans 32(9):1705– 1720

    Article  Google Scholar 

  21. Han XH, Chen YW, Xu G (2015) High-order statistics of weber local descriptors for image representation. IEEE Trans Cybern 45(6):1180–1193

    Article  Google Scholar 

  22. Yang G, Fang B, Tang Y (2017) Robust face recognition with multi-scale Weber local descriptor. Int J Wavelets Multiresolut Inf Process 15:1750052. [12 pages]

    Article  MathSciNet  Google Scholar 

  23. Wang B, Li W, Yang W, Liao Q (2011) IlluminationNormalizationBasedonWeber’sLaw WithApplicationtoFaceRecognition. IEEE Signal Processing LettersYear 18(8):462–465

    Article  Google Scholar 

  24. Wu Y, Jiang Y, Zhou Y et al (2014) Generalized Weber-face for illumination-robust face recognition. Neurocomputing 136:262–267

    Article  Google Scholar 

  25. Tran C-K, Pham D-T, Tseng C-D, Lee T-F (2018) Improving the face recognition accuracy under varying illumination conditions for local binary patterns and local ternary patterns based on weber-face and singular value decomposition. Adv Intell Syst Comput 579:84

    Google Scholar 

  26. Lee K-C, Ho J, Kriegman D (2005) Acquiring linear subspaces for face recognition under variable lighting. IEEE Trans Pattern Anal Mach Intel 27:684–698

    Article  Google Scholar 

  27. Sim T, Baker S, Bsat M (2003) The cmu pose, illumination, and expression database. IEEE Trans Pattern Anal Mach Intell 25(12):1615–1618

    Article  Google Scholar 

  28. Chen T, Zhou X S, Comaniciu D, Huang T S (2006) Total variation models for variable lighting face recognition. IEEE Trans Pattern Anal Mach Intell 28(9):1519–1524

    Article  Google Scholar 

  29. Liu F, Tang Z, Tang J (2013) WLBP: Weber local binary pattern for local image description. Neurocomputing 120:325–335

    Article  Google Scholar 

  30. Xie XH, Zheng WS, Liao JH, Yuen PC, Suen CY (2011) Normalization of face illumination based on large-and small-scale features. IEEE Trans Image Process 20:1807–1821

    Article  MathSciNet  MATH  Google Scholar 

  31. Zhang T, Tang Y, Fang B, Shang Z, Liu X (2009) Face recognition under varying illumination using gradient faces. IEEE Trans Image Process 18(11):2599–2606

    Article  MathSciNet  MATH  Google Scholar 

  32. Wang C P, Wei W, Zhang J S et al (2018) Robust discriminative feature learning with calibrated data reconstruction and sparse low-rank model. Appl Intell 48:156

    Article  Google Scholar 

  33. Huang W, Wang X, Jin Z et al (2015) Penalized collaborative representation based classification for face recognition. Appl Intell 43:722

    Article  Google Scholar 

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Authors and Affiliations

  1. University of Electronic Science and Technology of China, Chengdu, China

    Jianwen Chen, Zhen Zeng, Rumin Zhang, Wenyi Wang, Yao Zheng & Kun Tian

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  1. Jianwen Chen
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  2. Zhen Zeng
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  3. Rumin Zhang
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  4. Wenyi Wang
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  5. Yao Zheng
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  6. Kun Tian
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Correspondence to Rumin Zhang.

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Chen, J., Zeng, Z., Zhang, R. et al. Adaptive illumination normalization via adaptive illumination preprocessing and modified weber-face. Appl Intell 49, 872–882 (2019). https://doi.org/10.1007/s10489-018-1304-1

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  • DOI: https://doi.org/10.1007/s10489-018-1304-1

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