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Proceedings Paper

Adaptive skin detection based on online training
Author(s): Ming Zhang; Liang Tang; Jie Zhou; Gang Rong
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Paper Abstract

Skin is a widely used cue for porn image classification. Most conventional methods are off-line training schemes. They usually use a fixed boundary to segment skin regions in the images and are effective only in restricted conditions: e.g. good lightness and unique human race. This paper presents an adaptive online training scheme for skin detection which can handle these tough cases. In our approach, skin detection is considered as a classification problem on Gaussian mixture model. For each image, human face is detected and the face color is used to establish a primary estimation of skin color distribution. Then an adaptive online training algorithm is used to find the real boundary between skin color and background color in current image. Experimental results on 450 images showed that the proposed method is more robust in general situations than the conventional ones.

Paper Details

Date Published: 15 November 2007
PDF: 9 pages
Proc. SPIE 6788, MIPPR 2007: Pattern Recognition and Computer Vision, 678826 (15 November 2007); doi: 10.1117/12.750929
Show Author Affiliations
Ming Zhang, Tsinghua Univ. (China)
Liang Tang, Tsinghua Univ. (China)
Jie Zhou, Tsinghua Univ. (China)
Gang Rong, Tsinghua Univ. (China)

Published in SPIE Proceedings Vol. 6788:
MIPPR 2007: Pattern Recognition and Computer Vision

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