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

Asteriadis, S; Karpouzis, K; Kollias, S

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