A New Robust Color Descriptor for Face Detection

Eyal Braunstain, Isak Gath

2015

Abstract

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.

References

  1. Bergtholdt, M., Kappes, J., Schmidt, S., and Schnö rr, C. (2010). A study of parts-based object class detection using complete graphs. Int. J. Comput. Vision, 87(1- 2):93-117.
  2. Bindemann, M. and Burton, A. M. (2009). The role of color in human face detection. Cognitive Science, 33(6):1144-1156.
  3. Braunstain, E. and Gath, I. (2013). Combined supervised / unsupervised algorithm for skin detection: A preliminary phase for face detection. In Image Analysis and Processing - ICIAP 2013 - 17th International Conference, Naples, Italy, September 9-13, 2013. Proceedings, Part I, pages 351-360.
  4. Cai, J. and Goshtasby, A. A. (1999). Detecting human faces in color images. Image Vision Comput., 18(1):63-75.
  5. Cortes, C. and Vapnik, V. (1995). Support-vector networks. Machine Learning, 20(3):273-297.
  6. Diplaros, A., Gevers, T., and Patras, I. (2006). Combining color and shape information for illuminationviewpoint invariant object recognition. IEEE Transactions on Image Processing, 15:1-11.
  7. Felzenszwalb, P. F., Girshick, R. B., McAllester, D., and Ramanan, D. (2010). Object detection with discriminatively trained part-based models. IEEE Trans. Pattern Anal. Mach. Intell., 32:1627-1645.
  8. Gevers, T. and Smeulders, A. (1997). Color based object recognition. Pattern Recognition, 32:453-464.
  9. Guo, L. and Meng, Y. (2006). Psnr-based optimization of jpeg baseline compression on color images. In ICIP, pages 1145-1148. IEEE.
  10. Heisele, B., Ho, P., Wu, J., and Poggio, T. (2003). Face recognition: Component-based versus global approaches.
  11. hsuan Yang, M. and Ahuja, N. (1999). Gaussian mixture model for human skin color and its applications in image and video databases. In Its Application in Image and Video Databases. Proceedings of SPIE 99 (San Jose CA, pages 458-466.
  12. Huang, G. B., Ramesh, M., Berg, T., and Learned-Miller, E. (2007). Labeled faces in the wild: A database for studying face recognition in unconstrained environments. Technical Report 07-49, University of Massachusetts, Amherst.
  13. Jain, A. K. (1989). Fundamentals of Digital Image Processing. Prentice-Hall, Inc., Upper Saddle River, NJ, USA.
  14. Jain, V. and Learned-Miller, E. (2010). Fddb: A benchmark for face detection in unconstrained settings. Technical Report UM-CS-2010-009, University of Massachusetts, Amherst.
  15. Jones, M. J. and Rehg, J. M. (2002). Statistical color models with application to skin detection. Int. J. Comput. Vision, 46(1):81-96.
  16. Khan, F. S., Anwer, R. M., van de Weijer, J., Bagdanov, A. D., Vanrell, M., and Lopez, A. M. (2012a). Color attributes for object detection. In CVPR, pages 3306- 3313. IEEE.
  17. Khan, F. S., van de Weijer, J., and Vanrell, M. (2012b). Modulating shape features by color attention for object recognition. International Journal of Computer Vision, 98(1):49-64.
  18. Khan, R., van de Weijer, J., Khan, F. S., Muselet, D., Ducottet, C., and Barat, C. (2013). Discriminative color descriptors. In CVPR, pages 2866-2873. IEEE.
  19. Li, H., Hua, G., Lin, Z., Brandt, J., and Yang, J. (2013). Probabilistic elastic part model for unsupervised face detector adaptation. In The IEEE International Conference on Computer Vision (ICCV).
  20. Mikolajczyk, K., Schmid, C., and Zisserman, A. (2004). Human detection based on a probabilistic assembly of robust part detectors. In ECCV (1), volume 3021 of Lecture Notes in Computer Science, pages 69-82. Springer.
  21. Ojala, T., Pietikäinen, M., and Mäenpää, T. (2002). Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans. Pattern Anal. Mach. Intell., 24(7):971-987.
  22. Osuna, E., Freund, R., and Girosi, F. (1997). Training support vector machines: an application to face detection. pages 130-136.
  23. Overett, G., Petersson, L., Brewer, N., Pettersson, N., and Andersson, L. (2008). A new pedestrian dataset for supervised learning. In IEEE Intelligent Vehivles Symposium, Eindhoven, The Netherlands.
  24. Platt, J. C. (1999). Probabilistic outputs for support vector machines and comparisons to regularized likelihood methods. In ADVANCES IN LARGE MARGIN CLASSIFIERS, pages 61-74. MIT Press.
  25. Romdhani, S., Torr, P., and Schölkopf, B. (2004). Efficient face detection by a cascaded support-vector machine expansion. Royal Society of London Proceedings Series A, 460:3283-3297.
  26. Shechtman, E. and Irani, M. (2007). Matching local selfsimilarities across images and videos. In IEEE Conference on Computer Vision and Pattern Recognition 2007 (CVPR'07).
  27. Snoek, C. G. M. (2005). Early versus late fusion in semantic video analysis. In In ACM Multimedia, pages 399- 402.
  28. Terrillon, J.-C., Fukamachi, H., Akamatsu, S., and Shirazi, M. N. (2000). Comparative performance of different skin chrominance models and chrominance spaces for the automatic detection of human faces in color images. In FG, pages 54-63.
  29. van de Sande, K. E. A., Gevers, T., and Snoek, C. G. M. (2010). Evaluating color descriptors for object and scene recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, 32(9):1582-1596.
  30. Viola, P. and Jones, M. (2004). Robust real-time face detection. International Journal of Computer Vision, 57:137-154.
  31. Wei, Y., Sun, J., Tang, X., and Shum, H.-Y. (2007). Interactive offline tracking for color objects. In ICCV, pages 1-8.
  32. Weijer, J. V. D. and Schmid, C. (2006). Coloring local feature extraction. In In ECCV, 2006. MENSINK et al.: TMRF FOR IMAGE AUTOANNOTATION.
  33. Wolf, L., Hassner, T., and Taigman, Y. (2008). Descriptor based methods in the wild. In Real-Life Images workshop at the European Conference on Computer Vision (ECCV).
  34. Zarit, B. D., Super, B. J., and Quek, F. K. H. (1999). Comparison of five color models in skin pixel classification. In In ICCV99 Intl. Workshop on, pages 58-63.
  35. Zhang, L., Chu, R., Xiang, S., Liao, S., and Li, S. Z. (2007). Face detection based on multi-block lbp representation. In Proceedings of the 2007 international conference on Advances in Biometrics, ICB'07, pages 11- 18, Berlin, Heidelberg. Springer-Verlag.
  36. Zhu, X. and Ramanan, D. (2012). Face detection, pose estimation, and landmark localization in the wild. In CVPR, pages 2879-2886.
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Paper Citation


in Harvard Style

Braunstain E. and Gath I. (2015). A New Robust Color Descriptor for Face Detection . In Proceedings of the International Conference on Pattern Recognition Applications and Methods - Volume 2: ICPRAM, ISBN 978-989-758-077-2, pages 13-21. DOI: 10.5220/0005177400130021


in Bibtex Style

@conference{icpram15,
author={Eyal Braunstain and Isak Gath},
title={A New Robust Color Descriptor for Face Detection},
booktitle={Proceedings of the International Conference on Pattern Recognition Applications and Methods - Volume 2: ICPRAM,},
year={2015},
pages={13-21},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005177400130021},
isbn={978-989-758-077-2},
}


in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Pattern Recognition Applications and Methods - Volume 2: ICPRAM,
TI - A New Robust Color Descriptor for Face Detection
SN - 978-989-758-077-2
AU - Braunstain E.
AU - Gath I.
PY - 2015
SP - 13
EP - 21
DO - 10.5220/0005177400130021