A Similarity-Based Color Descriptor for Face Detection

  • Eyal BraunstainEmail author
  • Isak Gath
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9493)


Most state-of-the-art approaches to object and face detection rely on intensity information and ignore color information, as it usually exhibits variations due to illumination changes and shadows, and due to the lower spatial resolution in color channels than in the intensity image. We propose a new color descriptor, derived from a variant of Local Binary Patterns, designed to achieve invariance to monotonic changes in chroma. The descriptor is produced by histograms of encoded color texture similarity measures of small radially-distributed patches. As it is based on similarities of local patches, we expect the descriptor to exhibit a high degree of invariance to local appearance and pose changes. We demonstrate empirically by simulation the invariance of the descriptor to photometric variations, i.e. illumination changes and image noise, geometric variations, i.e. face pose and camera viewpoint, and discriminative power in a face detection setting. Lastly, we show that the contribution of the presented descriptor to face detection performance is significant and superior to several other color descriptors, which are in use for object detection. This color descriptor can be applied in color-based object detection and recognition tasks.


Face Image Local Binary Pattern Face Detection Image Patch Color Channel 
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.


  1. 1.
    Khan, F. S., Anwer, R. M., van de Weijer, J., Bagdanov, A. D., Vanrell, M., Lopez, A.M.: Color attributes for object detection. In: CVPR, pp. 3306–3313. IEEE (2012)Google Scholar
  2. 2.
    Viola, P., Jones, M.: Robust real-time face detection. Int. J. Comput. Vis. 57, 137–154 (2004)CrossRefGoogle Scholar
  3. 3.
    Mikolajczyk, K., Schmid, C., Zisserman, A.: Human detection based on a probabilistic assembly of robust part detectors. In: Pajdla, T., Matas, J.G. (eds.) ECCV 2004. LNCS, vol. 3021, pp. 69–82. Springer, Heidelberg (2004) CrossRefGoogle Scholar
  4. 4.
    Zhang, L., Chu, R.F., Xiang, S., Liao, S.C., 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) CrossRefGoogle Scholar
  5. 5.
    Li, H., Hua, G., Lin, Z., Brandt, J., Yang, J.: Probabilistic elastic part model for unsupervised face detector adaptation. In: The IEEE International Conference on Computer Vision (ICCV) (2013)Google Scholar
  6. 6.
    Bindemann, M., Burton, A.M.: The role of color in human face detection. Cogn. Sci. 33, 1144–1156 (2009)CrossRefGoogle Scholar
  7. 7.
    Wei, Y., Sun, J., Tang, X., Shum, H. Y.: Interactive offline tracking for color objects. In: ICCV, pp. 1–8 (2007)Google Scholar
  8. 8.
    Gevers, T., Smeulders, A.: Color based object recognition. Pattern Recogn. 32, 453–464 (1997)CrossRefGoogle Scholar
  9. 9.
    van de Weijer, J., Schmid, C.: Coloring local feature extraction. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006. LNCS, vol. 3952, pp. 334–348. Springer, Heidelberg (2006) CrossRefGoogle Scholar
  10. 10.
    Diplaros, A., Gevers, T., Patras, I.: Combining color and shape information for illumination-viewpoint invariant object recognition. IEEE Trans. Image Process. 15, 1–11 (2006)CrossRefGoogle Scholar
  11. 11.
    Khan, R., van de Weijer, J., Khan, F. S., Muselet, D., Ducottet, C., Barat, C.: Discriminative color descriptors. In: CVPR, pp. 2866–2873. IEEE (2013)Google Scholar
  12. 12.
    Van de Sande, K.E.A., Gevers, T., Snoek, C.G.M.: Evaluating color descriptors for object and scene recognition. IEEE Trans. Pattern Anal. Mach. Intell. 32, 1582–1596 (2010)CrossRefGoogle Scholar
  13. 13.
    Khan, F.S., van de Weijer, J., Vanrell, M.: Modulating shape features by color attention for object recognition. Int. J. Comput. Vis. 98, 49–64 (2012)CrossRefGoogle Scholar
  14. 14.
    Jain, A.K.: Fundamentals of Digital Image Processing. Prentice-Hall Inc, Upper Saddle River (1989) zbMATHGoogle Scholar
  15. 15.
    Felzenszwalb, P.F., Girshick, R.B., McAllester, D., Ramanan, D.: Object detection with discriminatively trained part-based models. IEEE Trans. Pattern Anal. Mach. Intell. 32, 1627–1645 (2010)CrossRefGoogle Scholar
  16. 16.
    Zhu, X., Ramanan, D.: Face detection, pose estimation, and landmark localization in the wild. In: CVPR, pp. 2879–2886 (2012)Google Scholar
  17. 17.
    Bergtholdt, M., Kappes, J., Schmidt, S., Schnörr, C.: A study of parts-based object class detection using complete graphs. Int. J. Comput. Vis. 87, 93–117 (2010)MathSciNetCrossRefGoogle Scholar
  18. 18.
    Heisele, B., Ho, P., Wu, J., Poggio, T.: Face recognition: component-based versus global approaches. J. Comput. Vis. Image Underst. - Spec. Issue Face Recogn. 91(1–2), 6–21 (2003)CrossRefGoogle Scholar
  19. 19.
    Wolf, L., Hassner, T., Taigman, Y.: Descriptor based methods in the wild. In: Real-Life Images Workshop at the European Conference on Computer Vision (ECCV) (2008)Google Scholar
  20. 20.
    Ojala, T., Pietikäinen, M., Mäenpää, T.: Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans. Pattern Anal. Mach. Intell. 24, 971–987 (2002)CrossRefzbMATHGoogle Scholar
  21. 21.
    Jain, V., Learned-Miller, E.: Fddb: a benchmark for face detection in unconstrained settings. Technical report UM-CS-2010-009. University of Massachusetts, Amherst (2010)Google Scholar
  22. 22.
    Shechtman, E., Irani, M.: Matching local self-similarities across images and videos. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2007) (2007)Google Scholar
  23. 23.
    Guo, L., Meng, Y.: Psnr-based optimization of jpeg baseline compression on color images. In: ICIP, pp. 1145–1148. IEEE (2006)Google Scholar
  24. 24.
    Snoek, C. G. M.: Early versus late fusion in semantic video analysis. In: In ACM Multimedia, pp. 399–402 (2005)Google Scholar
  25. 25.
    Huang, G. B., Ramesh, M., Berg, T., Learned-Miller, E.: Labeled faces in the wild: a database for studying face recognition in unconstrained environments. Technical report 07–49, University of Massachusetts, Amherst (2007)Google Scholar
  26. 26.
    Overett, G., Petersson, L., Brewer, N., Pettersson, N., Andersson, L.: A new pedestrian dataset for supervised learning. In: IEEE Intelligent Vehivles Symposium, Eindhoven, The Netherlands (2008)Google Scholar
  27. 27.
    Cortes, C., Vapnik, V.: Support-vector networks. Mach. Learn 20, 273–297 (1995)zbMATHGoogle Scholar
  28. 28.
    Romdhani, S., Torr, P., Schölkopf, B.: Efficient face detection by a cascaded support-vector machine expansion. R. Soc. Lond Proc. Ser. A 460, 3283–3297 (2004)CrossRefzbMATHMathSciNetGoogle Scholar
  29. 29.
    Osuna, E., Freund, R., Girosi, F.: Training support vector machines: an application to face detection, pp. 130–136 (1997)Google Scholar
  30. 30.
    Platt, J. C.: Probabilistic outputs for support vector machines and comparisons to regularized likelihood methods. In: Advances in Large Margin Classifiers, pp. 61–74. MIT Press (1999)Google Scholar
  31. 31.
    hsuan Yang, M., Ahuja, N.: Gaussian mixture model for human skin color and its applications in image and video databases. In: Proceedings of SPIE 1999 and its Application in Image and Video Databases, San Jose, CA, pp. 458–466 (1999)Google Scholar
  32. 32.
    Jones, M.J., Rehg, J.M.: Statistical color models with application to skin detection. Int. J. Comput. Vis. 46, 81–96 (2002)CrossRefzbMATHGoogle Scholar
  33. 33.
    Zarit, B.D., Super, B.J., Quek, F.K.H.: Comparison of five color models in skin pixel classification. In: International Workshop on ICCV 1999, pp. 58–63 (1999)Google Scholar
  34. 34.
    Terrillon, J. C., Fukamachi, H., Akamatsu, S., Shirazi, M. N.: Comparative performance of different skin chrominance models and chrominance spaces for the automatic detection of human faces in color images. In: FG, pp. 54–63 (2000)Google Scholar
  35. 35.
    Braunstain, E., Gath, I.: Combined supervised / unsupervised algorithm for skin detection: a preliminary phase for face detection. In: Petrosino, A. (ed.) ICIAP 2013, Part I. LNCS, vol. 8156, pp. 351–360. Springer, Heidelberg (2013) CrossRefGoogle Scholar
  36. 36.
    Cai, J., Goshtasby, A.A.: Detecting human faces in color images. Image Vis. Comput. 18, 63–75 (1999)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Faculty of Biomedical EngineeringTechnion - Israel Institute of TechnologyHaifaIsrael

Personalised recommendations