Visual-Context Boosting for Eye Detection

Song, M; Tao, D; Sun, Z; Li, X

Visual-Context Boosting for Eye Detection

Song, M Tao, D

Sun, Z Li, X

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Publication Type:: Journal Article
Citation:: IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, 2010
Issue Date:: 2010-02-17

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Field	Value	Language
dc.contributor.author	Song, M	en_US
dc.contributor.author	Tao, D https://orcid.org/0000-0001-7225-5449	en_US
dc.contributor.author	Sun, Z	en_US
dc.contributor.author	Li, X	en_US
dc.date.issued	2010-02-17	en_US
dc.identifier.citation	IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, 2010	en_US
dc.identifier.issn	1083-4419	en_US
dc.identifier.uri	http://hdl.handle.net/10453/15110
dc.description.abstract	Eye detection plays an important role in many practical applications. This paper presents a novel two-step scheme for eye detection. The first step models an eye by a newly defined visual-context pattern (VCP), and the second step applies semisupervised boosting for precise detection. VCP describes both the space and appearance relations between an eye region (region of eye) and a reference region (region of reference). The context feature of a VCP is extracted by using the integral image. Aiming to reduce the human labeling efforts, we apply semisupervised boosting, which integrates the context feature and the Haar-like features for precise eye detection. Experimental results on several standard face data sets demonstrate that the proposed approach is effective, robust, and efficient. We finally show that this approach is ready for practical applications.	en_US
dc.relation.ispartof	IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics	en_US
dc.relation.isbasedon	10.1109/TSMCB.2010.2040078	en_US
dc.subject.classification	Artificial Intelligence & Image Processing	en_US
dc.subject.mesh	Eye	en_US
dc.subject.mesh	Humans	en_US
dc.subject.mesh	Image Interpretation, Computer-Assisted	en_US
dc.subject.mesh	Biometry	en_US
dc.subject.mesh	Algorithms	en_US
dc.subject.mesh	Artificial Intelligence	en_US
dc.subject.mesh	Pattern Recognition, Automated	en_US
dc.title	Visual-Context Boosting for Eye Detection	en_US
dc.type	Journal Article
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
utslib.for	0102 Applied Mathematics	en_US
utslib.for	0906 Electrical and Electronic Engineering	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
utslib.copyright.status	closed_access
pubs.publication-status	Accepted	en_US

Abstract:

Eye detection plays an important role in many practical applications. This paper presents a novel two-step scheme for eye detection. The first step models an eye by a newly defined visual-context pattern (VCP), and the second step applies semisupervised boosting for precise detection. VCP describes both the space and appearance relations between an eye region (region of eye) and a reference region (region of reference). The context feature of a VCP is extracted by using the integral image. Aiming to reduce the human labeling efforts, we apply semisupervised boosting, which integrates the context feature and the Haar-like features for precise eye detection. Experimental results on several standard face data sets demonstrate that the proposed approach is effective, robust, and efficient. We finally show that this approach is ready for practical applications.

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http://hdl.handle.net/10453/15110