Simple and Robust Facial Portraits Recognition under Variable Lighting Conditions Based on Two-Dimensional Orthogonal Transformations

  • Paweł Forczmański
  • Georgy Kukharev
  • Nadezdha Shchegoleva
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8156)

Abstract

The paper addresses the problem of face recognition for images registered in variable lighting, which is common for real-world conditions. Presented algorithm is based on orthogonal transformation preceded by simple transformations comprising of equalization of brightness gradients, removal of spatial low frequency spectral components and fusion of spectral features depending on average pixels intensity. Two types of transformations: 2DDCT (two-dimensional Discrete Cosine Transform) and 2DKLT (two-dimensional Karhunen-Loeve Transform) were investigated in order to find the most optimal algorithm setup. The results of experiments conducted on Yale B and Yale B+ datasets show that a quite simple algorithm is capable of successful recognition without high computing power demand, as opposite to several more sophisticated methods presented recently.

Keywords

face recognition illumination compensation dimensionality reduction DCT PCA 

References

  1. 1.
    Chen, W., Er, M.J., Wu, S.: PCA and LDA in DCT domain. Pattern Recognition Letters 26, 2474–2482 (2005)CrossRefGoogle Scholar
  2. 2.
    Chen, W., Er, M.J., Wu, S.: Illumination compensation and normalization for robust face recognition using discrete cosine transform in logarithm domain. IEEE Trans. Syst. Man Cybern. Part B 36(2), 458–466 (2006)CrossRefGoogle Scholar
  3. 3.
    Tan, X., Triggs, B.: Preprocessing and Feature Sets for Robust Face Recognition. In: IEEE Conf. on Computer Vision and Pattern Recognition, CVPR 2007, pp. 1–8 (2007)Google Scholar
  4. 4.
    Xie, X., Zheng, W.-S., Lai, J., Yuen, P.C.: Face Illumination Normalization on Large and Small Scale Features. In: IEEE Conf. on Computer Vision and Pattern Recognition, CVPR 2008 Anchorage, pp. 1–8 (2008)Google Scholar
  5. 5.
    Abbas, A., Khalil, M.I., AbdelHay, S., Fahmy, H.M.A.: Illumination invariant face recognition in logarithm discrete cosine transform domain. In: IEEE Inter. Conf. of Image Processing, ICIP 2009, pp. 4157–4160 (2009)Google Scholar
  6. 6.
    Shao, M., Wang, Y.: Joint Features for Face Recognition under Variable Illuminations. In: Fifth Inter. Conf. on Image and Graphics, ICIG 2009, pp. 922–927 (2009)Google Scholar
  7. 7.
    Liau, H.F., Isa, D.: New Illumination Compensation Method for Face Recognition. Inter. Journal of Computer and Network Security 2(3), 308–321 (2010)Google Scholar
  8. 8.
    Han, H., Shan, S., Qing, L., Chen, X., Gao, W.: Lighting Aware Preprocessing for Face Recognition across Varying Illumination. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010, Part II. LNCS, vol. 6312, pp. 308–321. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  9. 9.
    Goel, T., Nehra, V., Vishwakarma, V.P.: Comparative Analysis of various Illumination Normalization Techniques for Face Recognition. Inter. Journal of Computer Applications 28(9), 1–7 (2011)CrossRefGoogle Scholar
  10. 10.
    Cao, X., Shen, W., Yu, L.G., Wang, W.L., Yang, J.Y., Zhang, Z.W.: Illumination invariant extraction for face recognition using neighboring wavelet coefficients. Pattern Recognition 45, 1299–1305 (2012)CrossRefGoogle Scholar
  11. 11.
    Choi, S., Choi, C.-H., Kwak, N.: Face recognition based on 2D images under illumination and pose variations. Pattern Recognition Letters 32, 561–571 (2011)CrossRefGoogle Scholar
  12. 12.
    The Extended Yale Face Database B, http://vision.ucsd.edu/~leekc/ExtYaleDatabase/ExtYaleB.html (accessed May 01, 2012)
  13. 13.
    Zhang, T., Fang, B., Tang, Y.Y., Shang, Z., Li, D., Lang, F.: Multiscale facial structure representation for face recognition under varying illumination. Pattern Recognition 42(2), 251–258 (2009)CrossRefMATHGoogle Scholar
  14. 14.
    Forczmański, P., Kukharev, G.: Comparative analysis of simple facial features extractors. Journal of Real Time Image Processing 1(4), 239–255 (2007)CrossRefGoogle Scholar
  15. 15.
    Hafed, Z.M., Levine, M.D.: Face Recognition Using the Discrete Cosine Transform. International Journal of Computer Vision 43(2), 167–188 (2001)CrossRefMATHGoogle Scholar
  16. 16.
    Schwerin, B., Paliwal, K.: Local-DCT features for facial recognition. In: 2nd Inter. Conf. on Signal Processing and Communication Systems, ICSPCS 2008, pp. 1–6 (2008)Google Scholar
  17. 17.
    Kukharev, G., Forczmański, P.: Facial images dimensionality reduction and recognition by means of 2DKLT. Machine Graphics & Vision 16(3/4), 401–425 (2007)Google Scholar
  18. 18.
    Forczmański, P., Kukharev, G., Shchegoleva, N.: An algorithm of face recognition under difficult lighting conditions. Przeglad Elektrotechniczny (Electrical Review) 10b, 201–205 (2012)Google Scholar
  19. 19.
    Jobson, J., Rahman, Z., Woodell, G.A.: Properties and performance of a Center/Surround Retinex. Trans. on Image Processing 6(3), 451–462 (1997)CrossRefGoogle Scholar
  20. 20.
    Chen, T., Yin, W., Zhou, X.S., Comaniciu, D., Huang, T.S.: Total variation models for variable lighting face recognition. TPAMI 28(9), 1519–1524 (2006)CrossRefGoogle Scholar
  21. 21.
    Struc, V., Vesnicer, B., Mihelic, F., Pavesic, N.: Removing illumination artifacts from face images using the nuisance attribute projection. In: ICASSP 2010, pp. 846–849 (2010)Google Scholar
  22. 22.
    Zhiming, L., Chengjun, L.: Fusion of color, local spatial and global frequency information for face recognition. Pattern Recognition 43(8), 2882–2890 (2010)CrossRefMATHGoogle Scholar
  23. 23.
    Akrouf, S., Sehili, M.A., Chakhchoukh, A., Mostefai, M., Youssef, C.: Face Recognition Using: PCA and DCT. In: Proceedings of the 2009 Fifth International Conference on MEMS NANO, Smart Systems, ICMENS 2009, pp. 15–19 (2009)Google Scholar
  24. 24.
    Vishwakarma, V.P., Pandey, S., Gupta, M.N.: An Illumination Invariant Accurate Face Recognition with Down Scaling of DCT Coefficients. Journal of Computing and Information Technology - CIT 18(1), 53–67 (2010)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Paweł Forczmański
    • 1
  • Georgy Kukharev
    • 1
  • Nadezdha Shchegoleva
    • 2
  1. 1.Faculty of Computer Science and Information SystemsWest Pomeranian University of TechnologySzczecinPoland
  2. 2.Saint Petersburg State Electrotechnical University (LETI)Russia

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