A Low Rank Model Based Improved Eye Detection Under Spectacles

Please use this identifier to cite or link to this item: http://hdl.handle.net/2080/2557

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DC Field	Value	Language
dc.contributor.author	Lazarus, M Z	-
dc.contributor.author	Gupta, S	-
dc.date.accessioned	2016-11-10T10:23:32Z	-
dc.date.available	2016-11-10T10:23:32Z	-
dc.date.issued	2016-10	-
dc.identifier.citation	IEEE 7th Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON),Columbia University, New York, USA, 20-22 October 2016	en_US
dc.identifier.uri	http://hdl.handle.net/2080/2557	-
dc.description	Copyright belongs to the proceeding publisher	en_US
dc.description.abstract	Eye detection is a primary step in many applications such as face recognition, iris recognition, driver fatigue detection, gaze tracking etc. Occlusion by spectacles, glare and secondary image formations deteriorate its performance. In this paper, we formulate the glare/reflection removal as a classification problem and employ Low rank decomposition technique to overcome these challenges. We provide an in-depth analysis by comparing various low rank decomposition formulations and propose a simple preprocessing step to improve the detection accuracy. Experimentation on CASIA NIR-VIS 2.0 facial database validates the proposed preprocessing method.	en_US
dc.publisher	IEEE	en_US
dc.subject	Eye detection	en_US
dc.subject	Low rank model	en_US
dc.subject	Spectacle reflection	en_US
dc.subject	Glare removal	en_US
dc.title	A Low Rank Model Based Improved Eye Detection Under Spectacles	en_US
dc.type	Article	en_US
Appears in Collections:	Conference Papers

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