Please use this identifier to cite or link to this item: https://doi.org/10.1049/iet-bmt.2012.0006
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dc.titleAdaptive discriminative metric learning for facial expression recognition
dc.contributor.authorYan, H.
dc.contributor.authorAng Jr., M.H.
dc.contributor.authorPoo, A.N.
dc.date.accessioned2014-06-17T06:10:35Z
dc.date.available2014-06-17T06:10:35Z
dc.date.issued2012-09
dc.identifier.citationYan, H., Ang Jr., M.H., Poo, A.N. (2012-09). Adaptive discriminative metric learning for facial expression recognition. IET Biometrics 1 (3) : 160-167. ScholarBank@NUS Repository. https://doi.org/10.1049/iet-bmt.2012.0006
dc.identifier.issn20474938
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/59364
dc.description.abstractThe authors propose in this study a new adaptive discriminative metric learning method for facial expression recognition. Although a number of methods have been proposed for facial expression recognition, most of them apply the conventional Euclidean distance metric to measure the similarity/dissimilarity of face expression images and cannot effectively characterise such similarity/dissimilarity of these images because the intrinsic space of face images usually do not lie in such an Euclidean space. Motivated by the fact that between-class facial images with small differences are more easily mis-classified than those with large differences, the authors propose learning an adaptive metric by imposing large penalties on between-class samples with small differences and small penalties on those samples with large differences simultaneously, such that more discriminative information can be extracted in the learned distance metric for facial expression recognition. Experimental results on three widely used face datasets are presented to demonstrate the efficacy of the proposed method. © 2012 The Institution of Engineering and Technology.
dc.description.urihttp://libproxy1.nus.edu.sg/login?url=http://dx.doi.org/10.1049/iet-bmt.2012.0006
dc.sourceScopus
dc.typeArticle
dc.contributor.departmentMECHANICAL ENGINEERING
dc.description.doi10.1049/iet-bmt.2012.0006
dc.description.sourcetitleIET Biometrics
dc.description.volume1
dc.description.issue3
dc.description.page160-167
dc.identifier.isiut000209167000002
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