Friday 22nd of September 2017
 

Fuzzy Based Skin Detection and Segmentation


Afia Nazir, Laiq Hassan, Alamzeb M. Zai and Rehanullah Khan

Skin detection being one of the most crucial tasks in image processing applications, is targeted in this work with special emphasis on fuzzy based skin detection and segmentation techniques. Using weka tool, this paper provides the performance evaluation of two well-known fuzzy based skin detection techniques, fuzzy inference system and Modified Fuzzy C-Mean algorithm (MFCM) on a standard dataset. For the inference system we used the Fuzzy Unordered Rule Induction Algorithm (FURIA) implementation, and Brute Force Search Algorithm implementation for the Modified Fuzzy C-Means algorithm. Based on the experimental results, fuzzy inference algorithm provides 80.4633 % of correct classification for skin pixels and MFCM has 85.2316 % of accuracy in identifying the skin pixels.

Keywords: Skin modeling, color space, Fuzzy inference systems, Skin pixels, MFCM, Fuzzy inference system, fuzzy classification.

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ABOUT THE AUTHORS

Afia Nazir
University of Engineering & Technology, Peshawar, Pakistan

Laiq Hassan
University of Engineering & Technology, Peshawar, Pakistan

Alamzeb M. Zai
Gandhara Institute of Science and Technology, Peshawar

Rehanullah Khan
Sarhad University of Science and IT


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