A data-mining based skin detection method in JPEG compressed domain

Authors:
Shiwei Zhao;Li Zhuo;Zhu Xiao;Lansun Shen
Affiliations:
Signal and Information Processing Laboratory, Beijing University of Technology, Beijing, China;Signal and Information Processing Laboratory, Beijing University of Technology, Beijing, China;Signal and Information Processing Laboratory, Beijing University of Technology, Beijing, China;Signal and Information Processing Laboratory, Beijing University of Technology, Beijing, China
Venue:
FSKD'09 Proceedings of the 6th international conference on Fuzzy systems and knowledge discovery - Volume 5
Year:
2009

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Abstract

A novel skin detection method in JPEG compressed domain has been proposed in this paper. Color and texture features of the image blocks are extracted from the entropy decoded DCT coefficients firstly. Then, data mining method, i.e. decision tree, is applied to establish the skin color model to describe the relationship between the features of image blocks and the skin detection results, afterwards, the initial skin regions are detected based on the skin color model. The skin regions are finally, segmented through region growing method. Experimental results show that, compared with the SPM (Skin Probability Map) skin detection method in the pixel domain, the proposed method can achieve higher detection accuracy as well as higher speed.