Performance analysis of ANN based YCbCr skin detection algorithm

Please use this identifier to cite or link to this item: http://repository.elizadeuniversity.edu.ng/jspui/handle/20.500.12398/470

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DC Field	Value	Language
dc.contributor.author	Aibinu, Abiodun M.	-
dc.contributor.author	Shafie, Amir A.	-
dc.contributor.author	Salami, Momoh-Jimoh E.	-
dc.date.accessioned	2019-08-14T14:14:58Z	-
dc.date.available	2019-08-14T14:14:58Z	-
dc.date.issued	2012-01-01	-
dc.identifier.citation	Aibinu, A. M., Shafie, A. A., & Salami, M. J. E. (2012). Performance analysis of ANN based YCbCr skin detection algorithm. Procedia Engineering, 41, 1183-1189.	en_US
dc.identifier.issn	1877-7058	-
dc.identifier.uri	10.1016/j.proeng.2012.07.299	-
dc.identifier.uri	http://repository.elizadeuniversity.edu.ng/jspui/handle/20.500.12398/470	-
dc.description.abstract	Skin detection from acquired images has various areas of applications especially in automatic facial and human recognition system. The performance analysis of artificial neural network based –YcbCr skin recognition and three other techniques is evaluated in this work. Results obtained show that the use of YCbCr color model performs better than RGB colour model and the use of artificial neural network further improves the accuracy of the system.	en_US
dc.language.iso	en	en_US
dc.publisher	Elsevier	en_US
dc.subject	Acquired Image	en_US
dc.subject	Artificial Neural	en_US
dc.subject	Network	en_US
dc.subject	Modeling	en_US
dc.subject	Technique	en_US
dc.subject	Skin	en_US
dc.title	Performance analysis of ANN based YCbCr skin detection algorithm	en_US
dc.type	Article	en_US
Appears in Collections:	Research Articles

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