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Journal of Information Science and Engineering, Vol. 23 No. 6, pp. 1737-1752 (November 2007)

Block Histogram-Based Neural Fuzzy Approach to the Segmentation of Skin Colors*

Chia-Feng Juang, Hwai-Sheng Perng and Shih-Hsuan Chiu
Department of Electrical Engineering
National Chung Hsing University
Taichung, 402 Taiwan

Skin color segmentation by a block histogram-based neural fuzzy network is proposed in this paper. The Hue-Saturation (HS) color model is used. Color information is represented by a block histogram in an HS space image. Several non-uniform quantization approaches on HS space are proposed to represent histogram information as accurately as possible. The neural fuzzy network used is the self-constructing neural fuzzy inference network (SONFIN). Block histogram information from images under different environments is used to train SONFIN to make the method as robust as possible. Experiments on skin color segmentation are performed to verify the performance of the proposed method. For comparison, three other segmentation methods, including principal component transformation (PCT), histogram-based skin classifier (HSC), and mixture of Gaussian classifier (MGC) are applied to the same problem. Comparisons show that the proposed approach achieves the best segmentation results. In addition, the proposed non-uniform HS partition approach also improves segmentation performance.

Keywords: neural networks, fuzzy systems, fuzzy neural network, histogram-based model, mixture of Gaussian model, irregular space partition

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Received September 15, 2005; revised June 30, 2006; accepted August 9, 2006.
Communicated by Chung-Yu Wu.
*A preliminary version has been presented in IEEE & INNS International Joint Conference on Neural Network, Montreal, Canada, Aug., 2005.