NDLI: A texture analysis approach to supervised face segmentation

Content Provider	IEEE Xplore Digital Library
Author	Laboreiro, V.R.S. de Araujo, T.P. Bessa Maia, J.E.
Copyright Year	2014
Description	Author affiliation: State Univ. of Ceara, Ceará, Brazil (Laboreiro, V.R.S.; de Araujo, T.P.; Bessa Maia, J.E.)
Abstract	This paper proposes to segment face images into six classes (eyes, nose, mouth, hair/eyebrows/beard, skin, and background) by classifying pixels based on the texture features calculated in a neighborhood of each pixel. Leung-Malik filter banks are applied to the color images for feature extraction and Random Projections are used to reduce data dimensionality. In order to perform pixel classification, manually labeled images are used to train a Multi-Quadric Radial Basis Function Neural Network, with centers selected by the Fast Condensed Nearest Neighbor algorithm. Quantitative and qualitative results are presented and demonstrate that the methodology can correctly segment most of the class labels with high effectiveness rate, comparable with the results achieved by state-of-art methods.
Starting Page	1
Ending Page	6
File Size	4601870
Page Count	6
File Format	PDF
ISBN	9781479942770
DOI	10.1109/ISCC.2014.6912548
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-06-23
Publisher Place	Portugal
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Feature extraction Face Image segmentation Vectors Nose Training Hair
Content Type	Text
Resource Type	Article

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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