A fusion approach for efficient human skin detection

Tan, W.R. and Chan, C.S. and Yogarajah, P. and Condell, J. (2012) A fusion approach for efficient human skin detection. IEEE Transactions on Industrial Informatics, 8 (1). pp. 138-147. ISSN 1551-3203,

Full text not available from this repository.
Official URL: http://ieeexplore.ieee.org/ielx5/9424/6133473/0605...


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: Article
Additional Information: Tan, Wei Ren Chan, Chee Seng Yogarajah, Pratheepan Condell, Joan
Uncontrolled Keywords: Face , Histograms , Humans , Image color analysis , Lighting , Skin , Training ,Gaussian processes , image fusion , image segmentation , lighting Gaussian model , automatic human skin detection , color images , eye detector , false skin detection , fusion strategy , human skin segmentation , human skin-color detection , illumination conditions , smoothed 2D histogram
Subjects: T Technology > T Technology (General)
Divisions: Faculty of Computer Science & Information Technology > Department of Artificial Intelligence
Depositing User: Miss Nur Jannatul Adnin Ahmad Shafawi
Date Deposited: 16 Apr 2013 01:26
Last Modified: 16 Apr 2013 01:26
URI: http://eprints.um.edu.my/id/eprint/5559

Actions (login required)

View Item View Item