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A Fusion Approach for Efficient Human Skin Detection

Biomedical Sciences Research Institute Computer Science Research Institute Environmental Sciences Research Institute Nanotechnology & Advanced Materials Research Institute

Tan, Wei Ren, Chan, Chee Seng, Yogarajah, Pratheepan and Condell, Joan (2012) A Fusion Approach for Efficient Human Skin Detection. IEEE Transactions on Industrial Informatics, 8 (1). pp. 138-147. [Journal article]

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URL: http://ieeexplore.ieee.org/xpl/freeabs_all.jsp?arnumber=6051482

DOI: 10.1109/TII.2011.2172451

Abstract

A reliable human skin detection method that is adaptable to different human skin colors and illumination conditions is essential for better human skin segmentation. Even though different human skin-color detection solutions have been successfully applied, they are prone to false skin detection and are not able to cope with the variety of human skin colors across different ethnic. Moreover, existing methods require high computational cost. In this paper, we propose a novel human skin detection approach that combines a smoothed 2-D histogram and Gaussian model, for automatic human skin detection in color image(s). In our approach, an eye detector is used to refine the skin model for a specific person. The proposed approach reduces computational costs as no training is required, and it improves the accuracy of skin detection despite wide variation in ethnicity and illumination.To the best of our knowledge, this is the first method to employ fusion strategy for this purpose. Qualitative and quantitative results on three standard public datasets and a comparison with state-of-the-art methods have shown the effectiveness and robustness of the proposed approach.

Item Type:Journal article
Keywords:Color space, dynamic threshold, fusion strategy, skin detection.
Faculties and Schools:Faculty of Computing & Engineering
Faculty of Computing & Engineering > School of Computing and Intelligent Systems
Research Institutes and Groups:Computer Science Research Institute
Computer Science Research Institute > Intelligent Systems Research Centre
ID Code:21027
Deposited By:Dr Joan Condell
Deposited On:10 Feb 2012 15:16
Last Modified:10 Feb 2012 15:16

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