Robust validation of Visual Focus of Attention using adaptive fusion of head and eye gaze patterns

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dc.contributor.author Asteriadis, S en
dc.contributor.author Karpouzis, K en
dc.contributor.author Kollias, S en
dc.date.accessioned 2014-03-01T02:53:27Z
dc.date.available 2014-03-01T02:53:27Z
dc.date.issued 2011 en
dc.identifier.uri https://dspace.lib.ntua.gr/xmlui/handle/123456789/36328
dc.subject.other Adaptive fusion en
dc.subject.other Background noise en
dc.subject.other Convolutional neural network en
dc.subject.other Eye-gaze en
dc.subject.other Face regions en
dc.subject.other Focus of Attention en
dc.subject.other Head pose en
dc.subject.other Head rotation en
dc.subject.other Human annotations en
dc.subject.other Local information en
dc.subject.other Modality Fusion en
dc.subject.other Specific areas en
dc.subject.other Test data en
dc.subject.other Fuzzy logic en
dc.subject.other Image processing en
dc.subject.other Neural networks en
dc.subject.other Rotation en
dc.subject.other Information use en
dc.title Robust validation of Visual Focus of Attention using adaptive fusion of head and eye gaze patterns en
heal.type conferenceItem en
heal.identifier.primary 10.1109/ICCVW.2011.6130271 en
heal.identifier.secondary http://dx.doi.org/10.1109/ICCVW.2011.6130271 en
heal.identifier.secondary 6130271 en
heal.publicationDate 2011 en
heal.abstract We propose a framework for inferring the focus of attention of a person, utilizing information coming both from head rotation and eye gaze estimation. To this aim, we use fuzzy logic to estimate confidence on the gaze of a person towards a specific point, and results are compared to human annotation. For head pose we propose Bayesian modality fusion of both local and holistic information, while for eye gaze we propose a methodology that calculates eye gaze directionality, removing the influence of head rotation, using a simple camera. For local information, feature positions are used, while holistic information makes use of face region. Holistic information uses Convolutional Neural Networks which have been shown to be immune to small translations and distortions of test data. This is vital for an application in an unpretending environment, where background noise should be expected. The ability of the system to estimate focus of attention towards specific areas, for unknown users, is grounded at the end of the paper. © 2011 IEEE. en
heal.journalName Proceedings of the IEEE International Conference on Computer Vision en
dc.identifier.doi 10.1109/ICCVW.2011.6130271 en
dc.identifier.spage 414 en
dc.identifier.epage 421 en

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