Please use this identifier to cite or link to this item: http://hdl.handle.net/2080/2837
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dc.contributor.authorModalavalasa, Sowjanya-
dc.contributor.authorSahoo, Upendra Kumar-
dc.contributor.authorSahoo, Ajit Kumar-
dc.date.accessioned2018-01-01T11:37:18Z-
dc.date.available2018-01-01T11:37:18Z-
dc.date.issued2017-12-
dc.identifier.citation14th IEEE India Council International Conference (INDICON-2017) Roorkee, Uttarakhand, India, 15 - 17 December, 2017en_US
dc.identifier.urihttp://hdl.handle.net/2080/2837-
dc.descriptionCopyright of this document belongs to proceedings publisher.en_US
dc.description.abstractThe Label Consistent K-Singular Value Decomposition (LC-KSVD) algorithm, which has been introduced recently has shown better results for learning a discriminative dictionary for recognition and classification by considering the label consistency constraint in the cost function. However this approach assumes Guassian distribution for the coding residual, which might not be true in the practical Face Recognition system due to lighting, occlusion/disguise and expression variations. In this paper, we propose a new Robust Label Consistent Dictionary Learning (RLC-DL) algorithm, which assumes that the coding residual and coefficients are identically distributed, independent and tries to find a Maximum Likelihood Estimation of the coding problem. The proposed algorithm is evaluated on the publicly available face datasets and it shows superior performance than state of the art algorithms available including LC-KSVD for face recognition.en_US
dc.subjectFace Recognitionen_US
dc.subjectRobust Label Consistent Dictionaryen_US
dc.subjectDictionary Learningen_US
dc.titleRobust Label Consistent Dictionary Learning for Face Recognitionen_US
dc.typeArticleen_US
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