[go: up one dir, main page]
More Web Proxy on the site http://driver.im/ Skip to main content
Springer Nature Link
Log in

Illumination normalization using fuzzy filter in DCT domain for face recognition

  • Original Article
  • Published:
International Journal of Machine Learning and Cybernetics Aims and scope Submit manuscript

Abstract

We develop a new approach of illumination normalization for face recognition under varying lighting conditions. The effect of illumination variations is in decreasing order over low-frequency discrete cosine transform (DCT) coefficients. The proposed approach is expected to nullify the effect of illumination variations as well as to preserve the low-frequency details of a face image in order to achieve a good recognition performance. This has been accomplished by using a fuzzy filter applied over the low-frequency DCT (LFDCT) coefficients. The ‘simple classification technique’ (k-nearest neighbor classification) is used to establish the performance improvement by present approach of illumination normalization under high and unpredictable illumination variations. Our fuzzy filter based illumination normalization approach achieves zero error rate on Yale face database B (named as Yale B database in this work) and CMU PIE database. An excellent performance is achieved on extended Yale B database. The present approach of illumination normalization is also tested on Yale face database which comprises of illumination variations together with expression variations and misalignment. Significant reduction in the error rate is achieved by the present approach on this database as well. These results establish the superiority of the proposed approach of illumination normalization, over the existing ones.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
£29.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price includes VAT (United Kingdom)

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18
Fig. 19
Fig. 20
Fig. 21

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

References

  1. Basri R, Jacobs DW (2000) Lambertian reflectance and linear subspaces. NEC Research Institute Technical Report: 2000-172R

  2. Basri R, Jacobs DW (2003) Lambertian reflectance and linear subspaces. IEEE Trans Pattern Anal Mach Intell 25(2):218–233

    Article  Google Scholar 

  3. Belhumeur PN, Kriegman DJ (1998) What is the set of images of an object under all possible illumination conditions. Int J Comput Vision 28(3):245–260

    Article  Google Scholar 

  4. Belhumeur PN, Hespanha JP, Kriegman DJ (1997) Eigenfaces vs. Fisherfaces-recognition using class specific linear projection. IEEE Trans Pattern Anal Mach Intell 19(7):711–720

    Article  Google Scholar 

  5. Brunelli R, Poggio T (1993) Face recognition-features vs. templates. IEEE Trans Pattern Anal Mach Intell 15(10):1042–1053

    Article  Google Scholar 

  6. Chen HF, Belhumeur PN, Jacobs DW (2000) In search of illumination invariants. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition 1: 254–261

  7. Chen W, Joo M, Wu S (2006) Illumination compensation and normalization for robust face recognition using discrete cosine transform in logarithm domain. IEEE Trans Syst Man Cybern-Part B: Cybern 36(2):458–466

    Article  Google Scholar 

  8. Cover TM, Hart PE (1967) Nearest neighbor pattern classification. IEEE Trans Inf Theor 13(1):21–27

    Article  MATH  Google Scholar 

  9. Djouadi A, Bouktache E (1997) A fast algorithm for the nearest-neighbor classifier. IEEE Trans Pattern Anal Mach Intell 19(3):277–281

    Article  Google Scholar 

  10. Extended Yale B face database, http://vision.ucsd.edu/~leekc/ExtYaleDatabase/ExtYaleB.html

  11. Georghiades AS, Belhumeur PN, Jacobs DW (2001) 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

    Article  Google Scholar 

  12. Gong X, Li XX, Feng L, Xia R (2012) A robust framework for face contour detection from clutter background. Int J Mach Learn Cybern 3(2):111–118

    Article  Google Scholar 

  13. Gonzalez RC, Woods RE (2006) Digital image processing. Pearson Education, India

    Google Scholar 

  14. Hafed ZM, Levine MD (2001) Face recognition using the discrete cosine transform. Int J Comput Vision 43(3):167–188

    Article  MATH  Google Scholar 

  15. Klir GJ, Yuan B (1995) Fuzzy sets and fuzzy logic-theory and application. Prentice Hall, New Jersey

    Google Scholar 

  16. Lee KC, Ho J, Kriegman DJ (2005) Acquiring linear subspaces for face recognition under variable lighting. IEEE Trans Pattern Anal Mach Intell 27(5):684–698

    Article  Google Scholar 

  17. Li SZ, Jain AK (2005) Handbook of face recognition. Springer, New York

    MATH  Google Scholar 

  18. Lou Z, Jin Z (2006) Novel adaptive nearest neighbor classifiers based on hit-distance. In: Proceedings of 18th International Conference on Pattern Recognition (ICPR 2006) 3: 87–90

  19. MATLAB reference manual, http://www.mathworks.com/access/helpdesk/help/toolbox/images

  20. Poon B, Amin MA, Yan H (2011) Performance evaluation and comparison of PCA based human face recognition methods for distorted images. Int J Mach Learn Cybern 2(4):245–259

    Article  Google Scholar 

  21. Samet H (2008) K-nearest neighbor finding using MaxNearestDist. IEEE Trans Pattern Anal Mach Intell 30(2):243–252

    Article  MathSciNet  Google Scholar 

  22. Savvides M, Kumar V (2003) Illumination normalization using logarithm transforms for face recognition. In: Proceedings of 4th International Conference on Audio and Video Based Biometric Person Authentication (AVBPA) LNCS 2688: 549–556

  23. Shan S, Gao W, Cao B, Zhao D (2003) Illumination normalization for robust face recognition against varying lighting conditions. In: Proceedings of IEEE Workshop on Analysis and Modeling of Faces and Gestures (AMFG): 157–164

  24. Shashua A, Raviv TR (2001) The quotient image-class-based re-rendering and recognition with varying illuminations. IEEE Trans Pattern Anal Mach Intell 23(2):129–139

    Article  Google Scholar 

  25. 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 

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

    Article  Google Scholar 

  27. Veenman CJ, Reinders MJT (2005) The nearest subclass classifier-A compromise between the nearest mean and nearest neighbor classifier. IEEE Trans Pattern Anal Mach Intell 23(9):1417–1429

    Article  Google Scholar 

  28. Vishwakarma VP, Gupta MN (2011) A new learning algorithm for single hidden layer feedforward neural networks. Int J Comput Appl 28(6):26–33

    Google Scholar 

  29. Vishwakarma VP, Pandey S, Gupta MN (2009) Adaptive histogram equalization and logarithm transform with rescaled low frequency DCT coefficients for illumination normalization. Int J Recent Trends Eng 1(1):318–322

    Google Scholar 

  30. Vishwakarma VP, Pandey S, Gupta MN (2007) A novel approach for face recognition using DCT coefficients re-scaling for illumination normalization. In: Proceedings of IEEE Conference on Advanced Computing & Communication (ADCOM 2007): 535–539

  31. Vishwakarma VP, Pandey S, Gupta MN (2010) An illumination invariant accurate face recognition with down scaling of DCT coefficients. J Comput Inf Technol 18(1):53–67

    Google Scholar 

  32. Wang H, Li SZ, Wang Y (2004) Face recognition under varying lighting conditions using self quotient image. In: Proceedings of IEEE Conference on Automatic Face and Gesture Recognition: 819–824

  33. Wang H, Li SZ, Wang Y (2004) Generalized quotient image. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition 2: 498–505

  34. Xie X, Zheng WS, Lai J, Yuen PC, Suen CY (2011) Normalization of face illumination based on large and small-scale features. IEEE Trans Image Process 20(7):1807–1821

    Article  MathSciNet  Google Scholar 

  35. Xu X, Liu W, Venkatesh S (2012) An innovative face image enhancement based on principle component analysis. Int J Mach Learn Cybern 3(4):259–267

    Article  Google Scholar 

  36. Yale B face database, http://cvc.yale.edu/projects/yalefacesB/yalefacesB.html

  37. Yale face database, http://cvc.yale.edu/projects/yalefaces/yalefaces.html

  38. Zadeh LA (1965) Fuzzy sets. Inf Control 8:338–353

    Article  MATH  MathSciNet  Google Scholar 

  39. Zhang L, Samaras D (2003) Face recognition under variable lighting using harmonic image exemplars. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition 1: 19–25

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

    Article  MathSciNet  Google Scholar 

  41. Zhao F, Huang Q, Gao W (2006) Image matching by normalized cross-correlation. In: Proceedings of International Conference on Acoustics, Speech and Signal Processing (ICASSP 2006) 2: 729–732

  42. Zhao J, Su Y, Wang D, Luo S (2003) Illumination ratio image-synthesizing and recognition with varying illuminations. Pattern Recognit Lett 24:2703–2710

    Article  Google Scholar 

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

    Article  Google Scholar 

  44. Zou X, Kittler J, Messer K (2007) Illumination Invariant Face Recognition-A Survey. In: Proceedings of 1st IEEE Conference on Biometric: Theory, Application and Systems: 1–8

Download references

Acknowledgments

The author would like to express his sincere gratitude to Yale University and CMU for the use of the Yale, Yale B, Extended Yale B and CMU PIE face databases. He would also like to thank the reviewers, whose comments helped to improve the quality of the paper significantly.

Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, Inderprastha Engineering College (Affiliated to Mahamaya Technical University, UP, India), Sahibabad, Ghaziabad, 201010, UP, India

    Virendra P. Vishwakarma

Authors
  1. Virendra P. Vishwakarma
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Virendra P. Vishwakarma.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Vishwakarma, V.P. Illumination normalization using fuzzy filter in DCT domain for face recognition. Int. J. Mach. Learn. & Cyber. 6, 17–34 (2015). https://doi.org/10.1007/s13042-013-0182-4

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s13042-013-0182-4

Keywords

Search

Navigation