Please use this identifier to cite or link to this item: http://repositorio.inesctec.pt/handle/123456789/6087
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dc.contributor.authorJoão Carlos Monteiroen
dc.contributor.authorJaime Cardosoen
dc.date.accessioned2018-01-14T21:01:15Z-
dc.date.available2018-01-14T21:01:15Z-
dc.date.issued2015en
dc.identifier.urihttp://repositorio.inesctec.pt/handle/123456789/6087-
dc.identifier.urihttp://dx.doi.org/10.3390/s150101903en
dc.description.abstractHumans perform and rely on face recognition routinely and effortlessly throughout their daily lives. Multiple works in recent years have sought to replicate this process in a robust and automatic way. However, it is known that the performance of face recognition algorithms is severely compromised in non-ideal image acquisition scenarios. In an attempt to deal with conditions, such as occlusion and heterogeneous illumination, we propose a new approach motivated by the global precedent hypothesis of the human brain's cognitive mechanisms of perception. An automatic modeling of SIFT keypoint descriptors using a Gaussian mixture model (GMM)-based universal background model method is proposed. A decision is, then, made in an innovative hierarchical sense, with holistic information gaining precedence over a more detailed local analysis. The algorithm was tested on the ORL, ARand Extended Yale B Face databases and presented state-of-the-art performance for a variety of experimental setups.en
dc.languageengen
dc.relation3889en
dc.relation5554en
dc.rightsinfo:eu-repo/semantics/openAccessen
dc.titleA Cognitively-Motivated Framework for Partial Face Recognition in Unconstrained Scenariosen
dc.typearticleen
dc.typePublicationen
Appears in Collections:CTM - Articles in International Journals

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