Abstract
In this paper we compare different computational strategies for skin detection. They differ in the type of data used in the training phase, the type of pre-processing done on the query image, and the level of visual information used. In particular, we define a high-level computational strategy, which uses a face detector in the pre-processing step. Two different implementations of it are proposed: one relies on an adaptive single gaussian model, the other a fixed threshold skin cluster detector on an illuminant-independent image representation. The experimental results on a heterogeneous dataset containing images acquired under uncontrolled lighting conditions show that the high-level strategies outperform low-level ones.
Chapter PDF
Similar content being viewed by others
Keywords
References
Terrillon, J.C., Shirazit, M., Fukamachi, H., Akamatsu, S.: Comparative performance of different skin chrominance models and chrominance spaces for the automatic detection of human faces in color images. In: Proc. 4th Int. Conf. Automatic Face and Gesture Recognition, pp. 54–61 (2000)
Caetano, T., Olabarriaga, S.D., Barone, D.A.C.: Performance evaluation of single and multiple-Gaussian models for skin color modelling. In: Proc. Brazilian Symp. Computer Graphics and Image Processing, pp. 275–282 (2002)
Gasparini, F., Corchs, S., Schettini, R.: Recall or precision oriented strategies for binary classification of skin pixels. Journal of Electronic Imaging 17(2), 1–15 (2008)
Viola, P., Jones, M.: Rapid object detection using a boosted cascade of simple features. In: Proc. of the Conference on Computer Vision and Pattern Recognition, pp. 511–518 (2001)
http://lear.inrialpes.fr/people/vandeweijer/code/ColorConstancy.zip
Skin Detection, http://clickdamage.com/sourcecode/index.html
Vezhnevets, V., Sazonov, V., Andreeva, A.: A Survey on Pixel-Based Skin Color Detection Techniques. In: Proc. Graphicon-2003, Moscow, Russia, pp. 85–92 (September 2003)
Yang, M.H., Ahuja, N.: Gaussian Mixture Model for Human Skin Colour and its Applications in Image and Video Databases. In: SPIE/EI&T Storage and Retrieval for Image and Video Databases, pp. 458–466 (1999)
Tsekeridou, S., Pitas, I.: Facial feature extraction in frontal views using biometric analogies. In: Proc. of the IX European Signal Processing Conference, vol. I, pp. 315–318 (1998)
Chai, D., Ngan, K.N.: Face segmentation using skin color map in videophone applications. IEEE Transactions on Circuits and Systems for Video Technology 9(4), 551–564 (1999)
Chai, D., Bouzerdoum, A.: A Bayesian approach to skin color classification in YCbCr color space. In: Proceedings TENCON 2000, vol. 2, pp. 421–424 (2000)
Jones, M.J., Rehg, J.M.: Statistical color models with application to skin detection. International Journal of Computer Vision 46(1), 81–96 (2002)
Kovac, J., Peer, P., Solina, F.: 2D versus 3D colour space face detection. In: Proc. 4th EURASIP Conf. Video Image Processing and Multimedia Communications, pp. 449–454 (2003)
Hsu, R., Abdel Mottaleb, M., Jain, A.K.: Face detection in colour images. IEEE Trans. Pattern Analysis and Machine Intelligence 24, 696–706 (2002)
Garcia, C., Tziritas, G.: Face detection using quantized skin colour regions merging and wavelet packet analysis. IEEE Trans. Multimedia 1, 264–277 (1999)
Gomez, G., Morales, E.F.: Automatic feature construction and a simple rule induction algorithm for skin detection. In: Proc. of the ICML workshop on Machine Learning in Computer Vision, pp. 31–38 (2002)
Hsieh, I.S., Fan, K.C., Lin, C.: A statistic approach to the detection of human faces in colour nature scene. Pattern Recognition 35, 1583–1596 (2002)
Witkin, A.P.: Scale-space filtering: a new approach to multi-scale description. In: Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing, pp. 150–153 (1984)
Bianco, S., Schettini, R.: Color Constancy Using Faces. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 65–72 (2012)
Wilcoxon, F.: Individual comparisons by ranking methods. Biometrics 1, 80–83 (1945)
Van de Weijer, J., Gevers, T., Gijsenij, A.: Edge-based color constancy. IEEE Transactions on Image Processing 16(9), 2207–2214 (2007)
Gijsenij, A., Gevers, T., Van de Weijer, J.: Computational color constancy: survey and experiments. IEEE Transactions on Image Processing 20(9), 2475–2489 (2011)
Zhu, Q., Cheng, K.T., Wu, C.T., Wu, Y.L.: Adaptive learning of an accurate skin-color model. In: Proceedings of the Sixth IEEE International Conference on Automatic Face and Gesture Recognition, pp. 37–42 (2004)
Brand, J., Mason, J.: A comparative assessment of three approaches to pixel-level human skin detection. In: Proc. IEEE Int. Conf. Pattern Recognition, vol. 1, pp. 1056–1059 (2000)
Brand, J., Mason, J., Roach, M., Pawlewski, M.: Enhancing face detection in colour images using a skin probability map. In: Proc. Int.Conf. Intelligent Multimedia, Video and Speech Processing, pp. 344–347 (2001)
Zarit, B., Super, B.J., Quek, F.K.H.: Comparison of five color models in skin pixel classification. In: Proc. Workshop on Recognition, Analysis, and Tracking of Faces and Gestures in Real-Time Systems, pp. 58–63 (1999)
Sigal, L., Sclaroff, S., Athitsos, V.: Skin color-based video segmentation under time-varying illumination. IEEE Trans. Pattern Analysis and Machine Intelligence 26(7), 862–877 (2004)
Viola, P., Jones, M.: Robust real-time face detection. International Journal of Computer Vision 57(2), 137–154 (2004)
ISO. Graphic technology - standard object colour spectra database for colour reproduction evaluation (socs). Technical Report ISO/TR 16066:2003(E) (2003)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Bianco, S., Gasparini, F., Schettini, R. (2013). Computational Strategies for Skin Detection. In: Tominaga, S., Schettini, R., Trémeau, A. (eds) Computational Color Imaging. CCIW 2013. Lecture Notes in Computer Science, vol 7786. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-36700-7_16
Download citation
DOI: https://doi.org/10.1007/978-3-642-36700-7_16
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-36699-4
Online ISBN: 978-3-642-36700-7
eBook Packages: Computer ScienceComputer Science (R0)