Abstract—Skin region detection plays a vital role in wide range of image processing applications. This paper proposes the human skin region detection using Particle Swarm Optimization (PSO) technique with the face detection application. It consists of two steps. In the first step, the input RGB color image is converted into CIEL*a*b color space. Then this is clustered by the Hillclimbing segmentation with K-Means clustering algorithm, which will be useful to find the number of clusters and the local optimal solutions. In the second step, these local solutions are further improved by PSO technique using YCbCr explicit skin color conditions in order to find the global solution. This global solution helps to detect the robust skin region. The experimental results have been compared with our existing works and the performance of face detection results shows the importance and efficiencies of the optimization technique.
Index Terms—Color Space, Face detection, Fuzzy C-Means, K-Means, Particle Swarm Optimization, Skin region.
R. Vijayanandh is the Research Scholar in Research and Development Centre, Bharathiar University, Coimbatore, Tamil Nadu, India (e-mail: rvanandh@gmail.com).
G. Balakrishnan is with the Department of Computer Science and Engineering, Indra Ganesan College of Engineering, Tiruchirappalli, Tamil Nadu, India (e-mail: balakrishnan.g@gmail.com).
Cite: R. Vijayanandh, and G. Balakrishnan, "Performance Analysis of Human Skin Region Detection Techniques with Face Detection Application," International Journal of Modeling and Optimization vol. 1, no. 3, pp. 236-242, 2011.
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