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An Efficient LBP-Based Descriptor for Facial Depth Images Applied to Gender Recognition Using RGB-D Face Data

  • Tri Huynh
  • Rui Min
  • Jean-Luc Dugelay
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7728)

Abstract

RGB-D is a powerful source of data providing the aligned depth information which has great potentials in improving the performance of various problems in image understanding, while Local Binary Patterns (LBP) have shown excellent results in representing faces. In this paper, we propose a novel efficient LBP-based descriptor, namely Gradient-LBP (G-LBP), specialized to encode the facial depth information inspired by 3DLBP, yet resolves its inherent drawbacks. The proposed descriptor is applied to gender recognition task and shows its superiority to 3DLBP in all the experimental setups on both Kinect and range scanner databases. Furthermore, a weighted combination scheme of the proposed descriptor for depth images and the state-of-the-art LBP U2 for grayscale images applied in gender recognition is proposed and evaluated. The result reinforces the effectiveness of the proposed descriptor in complementing the source of information from the luminous intensity. All the experiments are carried out on both the high quality 3D range scanner database - Texas 3DFR and images of lower quality obtained from Kinect - EURECOM Kinect Face Dataset to show the consistency of the performance on different sources of RGB-D data.

Keywords

Local Binary Pattern Depth Image Grayscale Image Luminous Intensity Gender Recognition 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    Ojala, T., Pietkäinen, M., Mäenpää, T.: Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns. IEEE Trans. Pattern Analysis and Machine Intelligence 24(7), 971–987 (2002)CrossRefGoogle Scholar
  2. 2.
    Ahonen, T., Hadid, A., Pietikäinen, M.: Face description with local binary patterns: Application to face recognition. PAMI 28(12), 2037–2041 (2006)CrossRefGoogle Scholar
  3. 3.
    Shan, C., Gong, S., McOwan, P.: Facial expression recognition based on Local Binary Patterns: A comprehensive study. Image and Vision Computing 27(6), 803–816 (2009)CrossRefGoogle Scholar
  4. 4.
    Gunay, A., Nabiyev, V.V.: Automatic age classification with LBP. In: International Symposium on Computer and Information Sciences - ISCIS (2008)Google Scholar
  5. 5.
    Lian, H.-C., Lu, B.-L.: Multi-view Gender Classification Using Local Binary Patterns and Support Vector Machines. In: Wang, J., Yi, Z., Żurada, J.M., Lu, B.-L., Yin, H. (eds.) ISNN 2006. LNCS, vol. 3972, pp. 202–209. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  6. 6.
    Farinella, G., Dugelay, J.-L.: Demographic classification: Do gender and ethnicity affect each other? In: IAPR International Conference on Informatics, Electronics & Vision, ICIEV 2012, Dhaka, Bangladesh (2012)Google Scholar
  7. 7.
    Guo, Z., Zhang, L., Zhang, D.: A completed modeling of local binary pattern operator for texture classification. IEEE Trans. Image Process. 19(6), 1657–1663 (2010)MathSciNetCrossRefGoogle Scholar
  8. 8.
    Yang, H., Wang, Y.: A LBP-based face recognition method with Hamming distance constraint. In: Proc. International Conference on Image and Graphics, pp. 645–649 (2007)Google Scholar
  9. 9.
    Fehr, J.: Rotational Invariant Uniform Local Binary Patterns For Full 3D Volume Texture Analysis. FINSIG (2007)Google Scholar
  10. 10.
    Zhao, G., Pietikäinen, M.: Dynamic texture recognition using volume local binary patterns. In: ECCV, Workshop on Dynamical Vision, pp. 12–23 (2006)Google Scholar
  11. 11.
    Lu, X., Chen, H., Jain, A.: Multimodal Facial Gender and Ethnicity Identification. In: Zhang, D., Jain, A.K. (eds.) ICB 2005. LNCS, vol. 3832, pp. 554–561. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  12. 12.
    Zhu, Y., Dariush, B., Fujimura, K.: Controlled human pose estimation from depth image streams. In: CVPR time-of-flight Workshop (2008)Google Scholar
  13. 13.
    Huang, Y., Wang, Y., Tan, T.: Combining statistics of geometrical and correlative features for 3D face recognition. In: Proceedings of the British Machine Vision Conference, pp. 879–888 (2006)Google Scholar
  14. 14.
    Jin, H., Liu, Q., Lu, H., Tong, X.: Face detection using improved lbp under Bayesian framework. In: Proceedings of the Third International Conference on Image and Graphics, pp. 306–309 (2004)Google Scholar
  15. 15.
    Ylioinas, J., Hadid, A., Pietikäinen, M.: Combining Contrast Information and Local Binary Patterns for Gender Classification. In: Heyden, A., Kahl, F. (eds.) SCIA 2011. LNCS, vol. 6688, pp. 676–686. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  16. 16.
    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
  17. 17.
    Mäkinen, E., Raisamo, R.: Evaluation of Gender Classification Methods with Automatically Detected and Aligned Faces. IEEE Trans. Pattern Anal. Mach. Intell. 30(3), 541–547 (2008)CrossRefGoogle Scholar
  18. 18.
    Gupta, S., Castleman, K.R., Markey, M.K., Bovik, A.C.: Texas 3D Face Recognition Database. In: IEEE Southwest Symposium on Image Analysis and Interpretation, Austin, TX, pp. 97–100 (2010)Google Scholar
  19. 19.
    EURECOM Kinect Face Dataset, http://RGB-D.eurecom.fr

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Tri Huynh
    • 1
  • Rui Min
    • 1
  • Jean-Luc Dugelay
    • 1
  1. 1.Department of Multimedia CommunicationsEURECOMSophia AntipolisFrance

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