Heterogeneous Face Recognition from Local Structures of Normalized Appearance

  • Shengcai Liao
  • Dong Yi
  • Zhen Lei
  • Rui Qin
  • Stan Z. Li
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5558)


Heterogeneous face images come from different lighting conditions or different imaging devices, such as visible light (VIS) and near infrared (NIR) based. Because heterogeneous face images can have different skin spectra-optical properties, direct appearance based matching is no longer appropriate for solving the problem. Hence we need to find facial features common in heterogeneous images. For this, first we use Difference-of-Gaussian filtering to obtain a normalized appearance for all heterogeneous faces. We then apply MB-LBP, an extension of LBP operator, to encode the local image structures in the transformed domain, and further learn the most discriminant local features for recognition. Experiments show that the proposed method significantly outperforms existing ones in matching between VIS and NIR face images.


Face Recognition Heterogeneous MB-LBP DoG 


  1. 1.
    Ahonen, T., Hadid, A., Pietikainen, M.: Face recognition with local binary patterns. In: Pajdla, T., Matas, J. (eds.) ECCV 2004. LNCS, vol. 3021, pp. 469–481. Springer, Heidelberg (2004)Google Scholar
  2. 2.
    Friedman, J., Hastie, T., Tibshirani, R.: Additive logistic regression: a statistical view of boosting. Technical report, Department of Statistics, Sequoia Hall, Stanford Univerity (July 1998)Google Scholar
  3. 3.
    Gross, R., Brajovic, V.: An image preprocessing algorithm for illumination invariant face recognition. In: Proc. 4th International Conference on Audio- and Video-Based Biometric Person Authentication, Guildford, UK, June 9-11 (2003)Google Scholar
  4. 4.
    Jacobs, D., Belhumeur, P., Basri, R.: Comparing images under variable illumination. In: Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 610–617 (1998)Google Scholar
  5. 5.
    Li, S.Z., Chu, R., Liao, S., Zhang, L.: Illumination invariant face recognition using near-infrared images. IEEE Transactions on Pattern Analysis and Machine Intelligence 29(4), 627–639 (2007)Google Scholar
  6. 6.
    Liao, S., Lei, Z., Li, S.Z., Yuan, X., He, R.: Structured ordinal features for appearance-based object representation. In: Proceedings of the IEEE International Workshop on Analysis and Modeling of Faces and Gestures, pp. 183–192 (2007)Google Scholar
  7. 7.
    Liao, S., Lei, Z., Zhu, X., Sun, Z., Li, S.Z., Tan, T.: Face recognition using ordinal features. In: Zhang, D., Jain, A.K. (eds.) ICB 2006. LNCS, vol. 3832, pp. 40–46. Springer, Heidelberg (2006)Google Scholar
  8. 8.
    Liao, S., Zhu, X., Lei, Z., Zhang, L., Li, S.Z.: Learning multi-scale block local binary patterns for face recognition. In: Lee, S.-W., Li, S.Z. (eds.) ICB 2007. LNCS, vol. 4642, pp. 828–837. Springer, Heidelberg (2007)Google Scholar
  9. 9.
    Lin, D., Tang, X.: Inter-modality face recognition. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006. LNCS, vol. 3954, pp. 13–26. Springer, Heidelberg (2006)Google Scholar
  10. 10.
    Lindeberg, T.: Scale-space theory: A basic tool for analysing structures at different scales. Journal of Applied Statistics 21, 225–270 (1994)Google Scholar
  11. 11.
    Lowe, D.G.: Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision 60, 91–110 (2004)Google Scholar
  12. 12.
    Moghaddam, B., Nastar, C., Pentland, A.: A Bayesain similarity measure for direct image matching. Media Lab Tech. Report No. 393, MIT (August 1996)Google Scholar
  13. 13.
    Nayar, S.K., Bolle, R.M.: Reflectance based object recognition. International Journal of Computer Vision 17(3), 219–240 (1996)Google Scholar
  14. 14.
    Ojala, T., Pietikainen, M., Harwood, D.: A comparative study of texture measures with classification based on feature distributions. Pattern Recognition 29(1), 51–59 (1996)Google Scholar
  15. 15.
    Ojala, T., Pietikainen, M., Maenpaa, M.: Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Transactions on Pattern Analysis and Machine Intelligence 24(7), 971–987 (2002)Google Scholar
  16. 16.
    Shashua, A., Raviv, T.R.: The quotient image: Class based re-rendering and recognition with varying illuminations. IEEE Transactions on Pattern Analysis and Machine Intelligence 23(2), 129–139 (2001)Google Scholar
  17. 17.
    Tan, X., Triggs, B.: Enhanced local texture feature sets for face recognition under difficult lighting conditions. In: Proceedings of the IEEE International Workshop on Analysis and Modeling of Faces and Gestures (2007)Google Scholar
  18. 18.
    Wang, H.T., Li, S.Z., Wang, Y.S.: Generalized quotient image. In: Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 498–505 (2004)Google Scholar
  19. 19.
    Yang, W., Yi, D., Lei, Z., Sang, J., Li, S.Z.: 2D-3D face matching using CCA. In: Proc. IEEE International Conference on Automatic Face and Gesture Recognition, Amsterdam, The Netherlands, September 17-19 (2008)Google Scholar
  20. 20.
    Ye, J., Xiong, T., Li, Q., Janardan, R., Bi, J., Cherkassky, V., Kambhamettu, C.: Efficient model selection for regularized linear discriminant analysis. In: Proceedings of the ACM Conference on Information and Knowledge Management, pp. 532–539 (2006)Google Scholar
  21. 21.
    Yi, D., Liu, R., Chu, R., Lei, Z., Li, S.Z.: Face matching from near infrared to visual images. In: Proceedings of the 2nd IAPR/IEEE International Conference on Biometrics, Seoul, Korea (August 2007)Google Scholar
  22. 22.
    Zhang, L., Chu, R., Xiang, S., Liao, S., Li, S.Z.: Face detection based on multi-block lbp representation. In: Lee, S.-W., Li, S.Z. (eds.) ICB 2007. LNCS, vol. 4642, pp. 11–18. Springer, Heidelberg (2007)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Shengcai Liao
    • 1
  • Dong Yi
    • 1
  • Zhen Lei
    • 1
  • Rui Qin
    • 1
  • Stan Z. Li
    • 1
  1. 1.Center for Biometrics and Security Research, Institute of AutomationChinese Academy of SciencesBeijingChina

Personalised recommendations