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A review and analysis of eye-gaze estimation systems, algorithms and performance evaluation methods in consumer platforms

dc.contributor.authorKar, Anuradha
dc.contributor.authorCorcoran, Peter
dc.date.accessioned2018-09-20T16:12:26Z
dc.date.available2018-09-20T16:12:26Z
dc.date.issued2017-01-01
dc.identifier.citationKar, Anuradha; Corcoran, Peter (2017). A review and analysis of eye-gaze estimation systems, algorithms and performance evaluation methods in consumer platforms. IEEE Access 5 , 16495-16519
dc.identifier.issn2169-3536
dc.identifier.urihttp://hdl.handle.net/10379/12146
dc.description.abstractIn this paper, a review is presented for the research on eye gaze estimation techniques and applications, which has progressed in diverse ways over the past two decades. Several generic eye gaze use-cases are identified: desktop, TV, head-mounted, automotive, and handheld devices. Analysis of the literature leads to the identification of several platform specific factors that influence gaze tracking accuracy. A key outcome from this review is the realization of a need to develop standardized methodologies for the performance evaluation of gaze tracking systems and achieve consistency in their specification and comparative evaluation. To address this need, the concept of a methodological framework for practical evaluation of different gaze tracking systems is proposed.
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)
dc.relation.ispartofIEEE Access
dc.rightsAttribution-NonCommercial-NoDerivs 3.0 Ireland
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/3.0/ie/
dc.subjecteye gaze
dc.subjectgaze estimation
dc.subjectaccuracy
dc.subjecterror sources
dc.subjectperformance evaluation
dc.subjectuser platforms
dc.subjectreal-time eye
dc.subjectdriver visual-attention
dc.subjectmultiple light-sources
dc.subjecthead-mounted camera
dc.subjecttracking system
dc.subjectsingle-camera
dc.subjectcomputer-interface
dc.subjectpupil detection
dc.subjectneural-network
dc.subjectcalibration
dc.titleA review and analysis of eye-gaze estimation systems, algorithms and performance evaluation methods in consumer platforms
dc.typeArticle
dc.identifier.doi10.1109/access.2017.2735633
dc.local.publishedsourcehttps://doi.org/10.1109/access.2017.2735633
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Except where otherwise noted, this item's license is described as Attribution-NonCommercial-NoDerivs 3.0 Ireland