A multi-cue-based algorithm for skin detection under varying illumination conditions

In this paper, we propose a new approach for skin detection in images taken of different people under various illumination conditions utilizing colors and image segmentation based on edge and region integration. The algorithm incorporates vector-based color edge detection, color quantization, and a new kind of region growing. We achieve satisfactory results that most skin areas are detected correctly and efficiently. Our main contribution lies in the combination of multiple cues and fusion of skin detection and image segmentation.

[1]  Carlo Tomasi,et al.  Color edge detection with the compass operator , 1999, Proceedings. 1999 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No PR00149).

[2]  Tieniu Tan,et al.  Mixture clustering using multidimensional histograms for skin detection , 2004, ICPR 2004.

[3]  Jitendra Malik,et al.  Normalized cuts and image segmentation , 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[4]  Nikolaos G. Bourbakis,et al.  A survey of skin-color modeling and detection methods , 2007, Pattern Recognit..

[5]  Combining Color , Texture and Contour Cues for Image Segmentation , .

[6]  John F. Canny,et al.  A Computational Approach to Edge Detection , 1986, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[7]  Dorin Comaniciu,et al.  Robust analysis of feature spaces: color image segmentation , 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[8]  Hsien-Che Lee,et al.  Detecting boundaries in a vector field , 1991, IEEE Trans. Signal Process..

[9]  Nuggehally Sampath Jayant,et al.  An adaptive clustering algorithm for image segmentation , 1989, International Conference on Acoustics, Speech, and Signal Processing,.

[10]  Jianping Fan,et al.  Automatic image segmentation by integrating color-edge extraction and seeded region growing , 2001, IEEE Trans. Image Process..

[11]  Shu-Yuan Chen,et al.  Color texture segmentation using feature distributions , 2002, Pattern Recognit. Lett..

[12]  Michael T. Orchard,et al.  Color quantization of images , 1991, IEEE Trans. Signal Process..

[13]  Daniel P. Huttenlocher,et al.  Efficient Graph-Based Image Segmentation , 2004, International Journal of Computer Vision.

[14]  Xavier Cufí,et al.  Yet Another Survey on Image Segmentation: Region and Boundary Information Integration , 2002, ECCV.

[15]  Alan L. Yuille,et al.  Region Competition: Unifying Snakes, Region Growing, and Bayes/MDL for Multiband Image Segmentation , 1996, IEEE Trans. Pattern Anal. Mach. Intell..