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A Parametric Discriminative Approach for Skin color Detection by Training Weak Learners on Normalized Chrominance and Luminance

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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2017
Authors:
Faisal Jamal Nasir, Nasir Ahmad, Syed Shadab Ali Shah
10.5120/ijca2017915275

Faisal Jamal Nasir, Nasir Ahmad and Syed Shadab Ali Shah. A Parametric Discriminative Approach for Skin color Detection by Training Weak Learners on Normalized Chrominance and Luminance. International Journal of Computer Applications 173(3):35-41, September 2017. BibTeX

@article{10.5120/ijca2017915275,
	author = {Faisal Jamal Nasir and Nasir Ahmad and Syed Shadab Ali Shah},
	title = {A Parametric Discriminative Approach for Skin color Detection by Training Weak Learners on Normalized Chrominance and Luminance},
	journal = {International Journal of Computer Applications},
	issue_date = {September 2017},
	volume = {173},
	number = {3},
	month = {Sep},
	year = {2017},
	issn = {0975-8887},
	pages = {35-41},
	numpages = {7},
	url = {http://www.ijcaonline.org/archives/volume173/number3/28318-2017915275},
	doi = {10.5120/ijca2017915275},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}
}

Abstract

This paper presents a novel approach for the detection of skin color in image or video, captured through ordinary web camera. The TSL color space is used due to its specialty in distinguishing among the skin and Non-skin color. To label the skin colors, a classifier based on adaboost algorithm has been trained. To validate the performance of the classifier, a database of skin colors was developed using different color tones ranging from fair to deep.

References

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Keywords

TSL, HCI, Adaboost, RGB2TSL.