1. OAR@UM
  2. Faculty of Engineering
  3. Department of Systems & Control Engineering
  4. Scholarly Works - FacEngSCE
Please use this identifier to cite or link to this item: https://www.um.edu.mt/library/oar/handle/123456789/128225
Full metadata record
DC FieldValueLanguage
dc.contributor.authorRanasekara Pathiranage, Nipun Sandamal-
dc.contributor.authorCristina, Stefania-
dc.contributor.authorCamilleri, Kenneth P.-
dc.date.accessioned2024-10-29T10:52:28Z-
dc.date.available2024-10-29T10:52:28Z-
dc.date.issued2024-10-
dc.identifier.citationSandamal, N., Cristina, S. & Camilleri, K. P. (2024): Robust Iris Centre Localisation for Assistive Eye-Gaze Tracking. In: Proceedings of the Joint visuAAL-GoodBrother Conference on trustworthy video- and audio-based assistive technologies – COST Action CA19121 - Network on Privacy-Aware Audio- and Video- Based Applications for Active and Assisted Living, pp. 83-89.en_GB
dc.identifier.urihttps://www.um.edu.mt/library/oar/handle/123456789/128225-
dc.description.abstractIn this research work, we address the problem of robust iris centre localization in unconstrained conditions as a core component of our eye-gaze tracking platform. We investigate the application of U-Net variants for segmentation-based and regression-based approaches to improve our iris centre localisation, which was previously based on Bayes’ classification. The achieved results are comparable to or better than the state-of-the-art, offering a drastic improvement over those achieved by the Bayes’ classifier, and without sacrificing the real-time performance of our eye-gaze tracking platform.en_GB
dc.language.isoenen_GB
dc.rightsinfo:eu-repo/semantics/openAccessen_GB
dc.subjectEye trackingen_GB
dc.subjectEye -- Movementsen_GB
dc.subjectHuman-computer interactionen_GB
dc.subjectAssistive computer technologyen_GB
dc.subjectDeep learning (Machine learning)en_GB
dc.titleRobust iris centre localisation for assistive eye-gaze trackingen_GB
dc.typeconferenceObjecten_GB
dc.rights.holderThe copyright of this work belongs to the author(s)/publisher. The rights of this work are as defined by the appropriate Copyright Legislation or as modified by any successive legislation. Users may access this work and can make use of the information contained in accordance with the Copyright Legislation provided that the author must be properly acknowledged. Further distribution or reproduction in any format is prohibited without the prior permission of the copyright holder.en_GB
dc.bibliographicCitation.conferencenameJoint visuAAL-GoodBrother Conference on trustworthy video-and audio-based assistive technologiesen_GB
dc.bibliographicCitation.conferenceplaceAlicante, Spain. 18-20/06/2024.en_GB
dc.description.reviewedpeer-revieweden_GB
Appears in Collections:Scholarly Works - FacEngSCE

Files in This Item:
File Description SizeFormat 
Robust Iris Centre Localisation for Assistive Eye-Gaze Tracking-2.pdf390.23 kBAdobe PDFView/Open
Show simple item record Statistics


Items in OAR@UM are protected by copyright, with all rights reserved, unless otherwise indicated.