REGION-BASED SKIN COLOR DETECTION
Rudra P. K. Poudel, Hammadi Nait-Charif, Jian J. Zhang, David Liu
2012
Abstract
Skin color provides a powerful cue for complex computer vision applications. Although skin color detection has been an active research area for decades, the mainstream technology is based on the individual pixels. This paper presents a new region-based technique for skin color detection which outperforms the current state-of-the-art pixel-based skin color detection method on the popular Compaq dataset (Jones and Rehg, 2002). Color and spatial distance based clustering technique is used to extract the regions from the images, also known as superpixels. In the first step, our technique uses the state-of-the-art non-parametric pixel-based skin color classifier (Jones and Rehg, 2002) which we call the basic skin color classifier. The pixel-based skin color evidence is then aggregated to classify the superpixels. Finally, the Conditional Random Field (CRF) is applied to further improve the results. As CRF operates over superpixels, the computational overhead is minimal. Our technique achieves 91.17% true positive rate with 13.12% false negative rate on the Compaq dataset tested over approximately 14,000 web images.
References
- Boykov, Y. and Kolmogorov, V. (2004). An experimental comparison of min-cut/max- flow algorithms for energy minimization in vision. IEEE Transactions on Pattern Analysis and Machine Intelligence, 26(9):1124-1137.
- Boykov, Y., Veksler, O., and Zabih, R. (2001). Fast approximate energy minimization via graph cuts. IEEE Transactions on Pattern Analysis and Machine Intelligence, pages 1222-1239.
- Brown, D., Craw, I., and Lewthwaite, J. (2001). A som based approach to skin detection with application in real time systems. In Proceedings of the British Machine Vision Conference, volume 2, pages 491-500.
- Fulkerson, B., Vedaldi, A., and Soatto, S. (2009). Class segmentation and object localization with superpixel neighborhoods. In Proceedings International Conference on Computer Vision, volume 5.
- Jedynak, B., Zheng, H., and Daoudi, M. (2003). Maximum entropy models for skin detection. In Energy Minimization Methods in Computer Vision and Pattern Recognition, pages 180-193.
- Jones, M. J. and Rehg, J. M. (2002). Statistical color models with application to skin detection. International Journal of Computer Vision, 46(1):81-96.
- Kakumanu, P., Makrogiannis, S., and Bourbakis, N. (2007). A survey of skin-color modeling and detection methods. Pattern Recognition, 40(3):1106-1122.
- Kawato, S. and Ohya, J. (2002). Automatic skin-color distribution extraction for face detection and tracking. In International Conference on Signal Processing, volume 2, pages 1415-1418. IEEE.
- Kolmogorov, V. and Zabih, R. (2004). What energy functions can be minimized via graph cuts? IEEE Transactions on Pattern Analysis and Machine Intelligence, 26(2):147-159.
- Kruppa, H., Bauer, M., and Schiele, B. (2002). Skin patch detection in real-world images. In Van Gool, L., editor, Pattern Recognition, volume 2449 of Lecture Notes in Computer Science, pages 109-116. Springer Berlin / Heidelberg.
- Moore, A. P., Prince, S., Warrell, J., Mohammed, U., and Jones, G. (2008). Superpixel lattices. In IEEE Conference on Computer Vision and Pattern Recognition.
- Pan, Z., Healey, G., Prasad, M., and Tromberg, B. (2003). Face recognition in hyperspectral images. IEEE Transactions on Pattern Analysis and Machine Intelligence, 25(12):1552-1560.
- Peer, P., Kovac, J., and Solina, F. (2003). Human skin colour clustering for face detection. In International Conference on Computer as a Tool.
- Phung, S. L., Chai, D., and Bouzerdoum, A. (2002). A universal and robust human skin color model using neural networks. In Proceedings of International Joint Conference on Neural Networks, volume 4, pages 2844- 2849.
- Ren, X. and Malik, J. (2003). Learning a classification model for segmentation. In IEEE International Conference on Computer Vision, volume 1.
- Sebe, N., Cohen, I., Huang, T., and Gevers, T. (2004). Skin detection: A bayesian network approach. In Proceedings of the 17th International Conference on Pattern Recognition, pages 903-906, Cambridge, UK.
- Shotton, J., Winn, J., Rother, C., and Criminisi, A. (2006). Textonboost: Joint appearance, shape and context modeling for multi-class object recognition and segmentation. Proceedings of European Conference on Computer Vision, pages 1-15.
- Soatto, S. (2009). Actionable information in vision. In Proceedings of the International Conference on Computer Vision, volume 25.
- Socolinsky, D. A., Selinger, A., and Neuheisel, J. D. (2003). Face recognition with visible and thermal infrared imagery. Computer Vision and Image Understanding, 91(1-2):72-114.
- 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 Fourth IEEE International Conference on Automatic Face and Gesture Recognition, page 54.
- Vedaldi, A. and Fulkerson, B. (2008). VLFeat: An open and portable library of computer vision algorithms. http://www.vlfeat.org.
- Vedaldi, A. and Soatto, S. (2008). Quick shift and kernel methods for mode seeking. Proceedings of European Conference on Computer Vision, pages 705-718.
- Wong, K. W., Lam, K. M., and Siu, W. C. (2003). A robust scheme for live detection of human faces in color images. Signal Processing: Image Communication, 18(2):103-114.
- Yang, M. H. and Ahuja, N. (1998). Detecting human faces in color images. In International Conference on Image Processing, 1998, volume 1, pages 127-130.
- Zhu, Q., Cheng, K. T., Wu, C. T., and Wu, Y. L. (2004). Adaptive learning of an accurate skin-color model. In Sixth IEEE International Conference on Automatic Face and Gesture Recognition, pages 37-42.
Paper Citation
in Harvard Style
P. K. Poudel R., Nait-Charif H., J. Zhang J. and Liu D. (2012). REGION-BASED SKIN COLOR DETECTION . In Proceedings of the International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2012) ISBN 978-989-8565-03-7, pages 301-306. DOI: 10.5220/0003801203010306
in Bibtex Style
@conference{visapp12,
author={Rudra P. K. Poudel and Hammadi Nait-Charif and Jian J. Zhang and David Liu},
title={REGION-BASED SKIN COLOR DETECTION},
booktitle={Proceedings of the International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2012)},
year={2012},
pages={301-306},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0003801203010306},
isbn={978-989-8565-03-7},
}
in EndNote Style
TY - CONF
JO - Proceedings of the International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2012)
TI - REGION-BASED SKIN COLOR DETECTION
SN - 978-989-8565-03-7
AU - P. K. Poudel R.
AU - Nait-Charif H.
AU - J. Zhang J.
AU - Liu D.
PY - 2012
SP - 301
EP - 306
DO - 10.5220/0003801203010306