A Comparative Study of Color Texture Features for Face Analysis

  • Seung Ho Lee
  • Hyungil Kim
  • Yong Man Ro
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7786)

Abstract

Although color texture features have proven to be highly effective for face analysis, the comparisons between the color texture features have not been presented in the literature. The aim of this paper is to find the best way for combining color and texture features for face analysis. For this purpose, four different approaches (proposed for face recognition or facial expression recognition) of extracting color texture features are reviewed and compared through extensive experiments. Experimental results show that the texture feature extracted using color vector can achieve the highest recognition performances for both face recognition and facial expression recognition, among the color texture features presented in this paper.

Keywords

Face analysis face recognition facial expression recognition color texture face descriptor 

References

  1. 1.
    Jun, B., Kim, T., Kim, D.: A Compact Local Binary Pattern Using Maximization of Mutual Information for Face Analysis. Pattern Recognition 44(3), 532–543 (2011)MATHMathSciNetCrossRefGoogle Scholar
  2. 2.
    Liu, C., Wechsler, H.: Gabor Feature Based Classification Using the Enhanced Fisher Linear Discriminant Model for Face Recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence 28(12), 2037–2041 (2006)CrossRefGoogle Scholar
  3. 3.
    Bartlett, M.S., Littlewort, G., Frank, M., Lainscsek, C., Fasel, I., Movellan, J.: Recognizing Facial Expression: Machine Learning and Application to Spontaneous Behavior. In: IEEE International Conference on Computer Vision and Pattern Recognition (2005)Google Scholar
  4. 4.
    Ahonen, T., Hadid, A.: Face Description with Local Binary Pattern: Application to Face Recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence 11(4), 467–476 (2002)Google Scholar
  5. 5.
    Shan, C., Gong, S., McOwan, P.W.: Facial Expression Recognition Based on Local Binary Patterns: A Comparative Study. Image and Vision Computing 27(6), 803–816 (2009)CrossRefGoogle Scholar
  6. 6.
    Moore, S., Bowden, R.: Local Binary Patterns for Multi-view Facial Expression Recognition. Computer Vision Image Undertanding 115(4), 541–558 (2011)CrossRefGoogle Scholar
  7. 7.
    Choi, J.Y., Ro, Y.M., Plataniotis, K.N.: Color Face Recognition for Degraded Face Images. IEEE Transactions on Systems, Man and Cybernetics-Part B 39(5), 1217–1230 (2009)CrossRefGoogle Scholar
  8. 8.
    Choi, J.Y., Ro, Y.M., Plataniotis, K.N.: Color Local Texture Features for Color Face Recognition. IEEE Transactions on Image Processing 21(3), 1366–1380 (2012)MathSciNetCrossRefGoogle Scholar
  9. 9.
    Lajevardi, S.M., Hussain, Z.M.: Emotion Recognition from Color Facial Images Based on Multilinear Image Analysis and Log-Gabor Filters. In: International Conference on Image Vision Computing (2010)Google Scholar
  10. 10.
    Lajevardi, S.M., Wu, H.R.: Facial Expression Recognition in Perceptual Color Space. IEEE Transactions on Image Processing 21(8), 3721–3733 (2012)MathSciNetCrossRefGoogle Scholar
  11. 11.
    Choi, J.Y., Ro, Y.M., Plataniotis, K.N.: Using Colour Local Binary Pattern Features for Face Recognition. In: IEEE International Conference on Image Processing (2010)Google Scholar
  12. 12.
    Huang, Z., Zhang, W., Huang, H., Hou, L.: Using Gabor Filters Features for Multi-Pose Face Recognition in Color Images. In: Second International Symposium on Intelligent Information Technology Application (2008)Google Scholar
  13. 13.
    Jain, A., Healey, G.: A Multiscale Representation Including Opponent Color Features for Texture Recognition. IEEE Transactions on Image Processing 7(1), 124–128 (1998)CrossRefGoogle Scholar
  14. 14.
    Lukac, R., Smolka, B., Martin, K., Plataniotis, K.N., Venetsanopoulos, A.N.: Vector Filtering for Color Imaging. IEEE Signal Processing Magazine 22(1) (2005)Google Scholar
  15. 15.
    Lee, S.H., Choi, J.Y., Ro, Y.M., Plataniotis, K.N.: Local Color Vector Binary Patterns from Multichannel Face Images for Face Recognition. IEEE Transactions on Image Processing 21(4), 2347–2353 (2012)MathSciNetCrossRefGoogle Scholar
  16. 16.
    Shen, L., Bai, L.: A Review on Gabor Wavelets for Face Recognition. Pattern Analysis and Applications 9(2-3), 273–292 (2006)MathSciNetCrossRefGoogle Scholar
  17. 17.
    Huang, D., Shan, C., Ardabilian, M., Wang, Y., Chen, L.: Local Binary Patterns and Its Application to Facial Image Analysis: A Survey. IEEE Transactions on Systems, Man and Cybernetics-Part C 41(6), 765–781 (2011)CrossRefGoogle Scholar
  18. 18.
    Zou, J., Ji, Q., Nagy, G.: A Comparative Study of Local Matching Approach for Face Recognition. IEEE Transactions on Image Processing 16(10), 2617–2628 (2007)MathSciNetCrossRefGoogle Scholar
  19. 19.
    Jain, A., Nandakumar, K., Ross, A.: Score Normalization in Multimodal Biometric Systems. Pattern Recognition 38(12), 2270–2285 (2005)CrossRefGoogle Scholar
  20. 20.
    Martinez, A.M., Benavente, R.: The AR Face Database. CVC Technical Report 24 (1998) Google Scholar
  21. 21.
    Phillips, P.J., Moon, H., Rizvi, S.A., Rauss, P.J.: The FERET Evaluation Methodology for Face Recognition Algorithms. IEEE Transactions on Pattern Analysis and Machine Intelligence 22(10), 1090–1104 (2000)CrossRefGoogle Scholar
  22. 22.
    Gross, R., Matthews, I., Cohn, J., Kanade, T., Baker, S.: Multi-PIE. Image and Vision Computing 28(5), 807–813 (2010)CrossRefGoogle Scholar
  23. 23.
    Belhumeur, P.N., Hesphanha, J.P., Kriegman, D.J.: Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection. IEEE Transactions on Pattern Analysis and Machine Intelligence 9(7), 711–720 (1997)CrossRefGoogle Scholar
  24. 24.
    Lu, J., Plataniotis, K.N., Venetsanopoulos, A.N.: Regularized Discriminant Analysis for the Small Sample Size Problem in Face Recognition. Pattern Recognition Letter 24(16), 3079–3087 (2003)CrossRefGoogle Scholar
  25. 25.
    Mäenpää, T.: The Local Binary Pattern Approach to Texture Analysis-Extensions and Applications. Ph.D. Dissertation, University of Oulu (2003) Google Scholar
  26. 26.
    Field, D.J.: Relations Between the Statistics of Natural Images and the Response Properties of Cortical Cells. Journal of the Optical Society of America A 4(12), 2379–2394 (1987)CrossRefGoogle Scholar
  27. 27.
    Palm, C., Keysers, D., Lehmann, T., Spitzer, K.: Gabor Filtering of Complex Hue/Saturation Images for Color Texture Classification. In: International Conference on Computer Vision (2000)Google Scholar
  28. 28.
    Mäenpää, T., Pietikäinen, M.: Classification with Color and Texture: Jointly or Separately? Pattern Recognition 37, 1629–1640 (2004)CrossRefGoogle Scholar
  29. 29.
    Vasilescu, M.A.O., Terzopoulos, D.: Multilinear Image Analysis for Facial Recognition. In: International Conference on Pattern Recognition (2002)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Seung Ho Lee
    • 1
  • Hyungil Kim
    • 1
  • Yong Man Ro
    • 1
  1. 1.Image and Video Systems Lab.Korea Advance Institute of Science and Technology (KAIST)Yuseong-guRepublic of Korea

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