ROBUST HUMAN SKIN DETECTION IN COMPLEX ENVIRONMENTS

Ehsan Fazl Ersi, John Zelek

2006

Abstract

Skin detection has application in people retrieval, face detection/tracking, hand detection/tracking and more recently on face recognition. However, most of the currently available methods are not robust enough for dealing with some real-world conditions, such as illumination variation and background noises. This paper describes a novel technique for skin detection that is capable of achieving high performance in complex environments with real-world conditions. Three main contributions of our work are: (i) processing each pixel in different brightness levels for handling the problem of illumination variation, (ii) proposing a fast and simple method for incorporating the neighborhood information in processing each pixel, and (iii) presenting a comparative study on thresholding the skin likelihood map, and employing a local entropy technique for binarizing our skin likelihood map. Experiments on a set of real-world images and the comparison with some state-of-the-art methods validate the robustness of our method.

References

  1. Aggarwal, J.K.; Cai, Q., 1997. Human motion analysis: a review. In Nonrigid and Articulated Motion Workshop 1997, IEEE Proceedings.
  2. Alibiol, A. and Torres, L., 2001. Unsupervised color image segmentation algorithm for face detection applications. In Proc. 3rd IEEE International Conference on Image Processing.
  3. Comaniciu, D. and Ramesh, V., 2000. Robust detection and tracking of human faces with an active camera. In Proc. 3rd IEEE International Workshop on Visual Surveillance.
  4. Dai, Y. and Nakano, Y., 2002. Face-texture model-based on SGLD and its application in face detection in a color scene. Pattern Recognition, 29(6).
  5. Funt, B. and Barnard, K., 1998. Is machine color constancy good enough?. In Proc. 5th European Conference on Computer Vision
  6. Hsu, R. L., Abdel-Mottaleb, M. and Jain AK., A. K.,2002. Face detection in color images. IEEE Transactions on Pattern Analysis and Machine Intelligence, 24(5).
  7. Jayaram, S.; Schmugge, S.; Shin, M.C.; Tsap, L.V., 2004. Effect of colorspace transformation, the illuminance component, and color modeling on skin detection. In CVPR 2004, Proceedings of the 2004 IEEE Computer Society Conference Volume 2.
  8. Jones, M.J. and Rehg, J.M., 2002. Statistical color models with application to skin detection. International Journal of Computer Vision, 46(1).
  9. Pal, N. and Pal, S., 1989. Entropic thresholding. In Sygnal Process.
  10. Piirainen, T., Silven, O. and Tuulos, V., 2000. Layered selforganizing maps based video content classification. Workshop on Real-time Image Sequence Analysis.
  11. Martinkauppi, B., 2002. Face color under varying illumination - analysis and applications. Ph.D. thesis, University of Oulu.
  12. Martinkauppi, B.; Soriano, M.; Pietikainen, M., 2003. Detection of skin color under changing illumination: a comparative study. In Image Analysis and Processing, 2003.Proceedings. 12th International Conference.
  13. Ming-Hsuan, Y.; Kriegman, D.J.; Ahuja, N., 2002. Detecting faces in images: a survey. In Pattern Analysis and Machine Intelligence, IEEE Transactions, vol 24.
  14. Otsu, N., 1979. A threshold selection method from graylevel histograms. In IEEE Trans. Syst. Man Cybern.
  15. Ruiz-Del-Solar, J. and Verschae, R., 2004. Robust skin segmentation using neighborhood information. In. Proc.IEEE International Conference on Image Processing (ICIP).
  16. Ruiz-del-Solar, J. and Verschae, R., 2004. Skin detection using neighborhood information. In Automatic Face and Gesture Recognition, 2004 (FGR). Proceedings. Sixth IEEE International Conference.
  17. Son, L.M., Chai, D. and Bouzerdoum, A., 2001. A universal and robust human skin color model using neural networks. Proc. IJCNN 7801 International Joint Conference on Neural Networks, vol. 4.
  18. Stern, H; Efros, B., 2002. Adaptive color space switching for face tracking in multi-colored lighting environments. In Automatic Face and Gesture Recognition, 2002. Proceedings. Fifth IEEE International Conference.
  19. Viola, P. and Jones, M., 2001. Rapid object detection using a boosted cascade of simple features. In Proc. IEEE CVPR.
  20. Xiaojin, Z.; Jie Y.; Waibel, A., 2000. Segmenting hands of arbitrary color. In Automatic Face and Gesture Recognition Proceedings. Fourth IEEE International Conference.
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Paper Citation


in Harvard Style

Fazl Ersi E. and Zelek J. (2006). ROBUST HUMAN SKIN DETECTION IN COMPLEX ENVIRONMENTS . In Proceedings of the First International Conference on Computer Vision Theory and Applications - Volume 2: VISAPP, ISBN 972-8865-40-6, pages 27-34. DOI: 10.5220/0001376300270034


in Bibtex Style

@conference{visapp06,
author={Ehsan Fazl Ersi and John Zelek},
title={ROBUST HUMAN SKIN DETECTION IN COMPLEX ENVIRONMENTS},
booktitle={Proceedings of the First International Conference on Computer Vision Theory and Applications - Volume 2: VISAPP,},
year={2006},
pages={27-34},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0001376300270034},
isbn={972-8865-40-6},
}


in EndNote Style

TY - CONF
JO - Proceedings of the First International Conference on Computer Vision Theory and Applications - Volume 2: VISAPP,
TI - ROBUST HUMAN SKIN DETECTION IN COMPLEX ENVIRONMENTS
SN - 972-8865-40-6
AU - Fazl Ersi E.
AU - Zelek J.
PY - 2006
SP - 27
EP - 34
DO - 10.5220/0001376300270034