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Face classification using color information

Sajjanhar, Atul and Mohammed, Ahmed Abdulateef 2017, Face classification using color information, Information, vol. 8, no. 4, pp. 1-14, doi: 10.3390/info8040155.

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Title Face classification using color information
Author(s) Sajjanhar, AtulORCID iD for Sajjanhar, Atul orcid.org/0000-0002-0445-0573
Mohammed, Ahmed AbdulateefORCID iD for Mohammed, Ahmed Abdulateef orcid.org/0000-0002-3026-095X
Journal name Information
Volume number 8
Issue number 4
Article ID 155
Start page 1
End page 14
Total pages 14
Publisher MDPI AG
Place of publication Basel, Switzerland
Publication date 2017-11
ISSN 2078-2489
Keyword(s) gender classification
race classification
texture analysis
color models
Summary Color models are widely used in image recognition because they represent significant information. On the other hand, texture analysis techniques have been extensively used for facial feature extraction. In this paper; we extract discriminative features related to facial attributes by utilizing different color models and texture analysis techniques. Specifically, we propose novel methods for texture analysis to improve classification performance of race and gender. The proposed methods for texture analysis are based on Local Binary Pattern and its derivatives. These texture analysis methods are evaluated for six color models (hue, saturation and intensity value (HSV); L*a*b*; RGB; YCbCr; YIQ; YUV) to investigate the effect of each color model. Further, we configure two combinations of color channels to represent color information suitable for gender and race classification of face images. We perform experiments on publicly available face databases. Experimental results show that the proposed approaches are effective for the classification of gender and race.
Language eng
DOI 10.3390/info8040155
HERDC Research category C1 Refereed article in a scholarly journal
ERA Research output type C Journal article
Copyright notice ©2017, by the authors.
Persistent URL http://hdl.handle.net/10536/DRO/DU:30105175

Document type: Journal Article
Collections: School of Information Technology
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Created: Mon, 04 Dec 2017, 15:38:08 EST

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