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dc.contributor.authorMayo, Michael
dc.contributor.authorZhang, Edmond Yiwen
dc.coverage.spatialGenoa, Italy
dc.date.accessioned2009-05-20T23:25:09Z
dc.date.available2009-05-20T23:25:09Z
dc.date.issued2009
dc.identifier.citationMayo, M., & Zhang, E. Y. (2009). 3D face recognition using multiview keypoint matching. In Proceedings of the Sixth IEEE International Conference on Advanced Video and Signal Based Surveillance, 2009. AVSS ’09. (pp. 290–295). Washington, DC, USA: IEEE. http://doi.org/10.1109/AVSS.2009.11en
dc.identifier.urihttp://hdl.handle.net/10289/2170
dc.description.abstractA novel algorithm for 3D face recognition based point cloud rotations, multiple projections, and voted keypoint matching is proposed and evaluated. The basic idea is to rotate each 3D point cloud representing an individual’s face around the x, y or z axes, iteratively projecting the 3D points onto multiple 2.5D images at each step of the rotation. Labelled keypoints are then extracted from the resulting collection of 2.5D images, and this much smaller set of keypoints replaces the original face scan and its projections in the face database. Unknown test faces are recognised firstly by performing the same multiview keypoint extraction technique, and secondly, the application of a new weighted keypoint matching algorithm. In an extensive evaluation using the GavabDB 3D face recognition dataset (61 subjects, 9 scans per subject), our method achieves up to 95% recognition accuracy for faces with neutral expressions only, and over 90% accuracy for face recognition where expressions (such as a smile or a strong laugh) and random faceoccluding gestures are permitted.en
dc.format.mimetypeapplication/pdf
dc.language.isoen
dc.publisherIEEE
dc.relation.urihttp://www.avss09.org/en
dc.rights©2009 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.en
dc.subjectcomputer scienceen
dc.subject3D face recognitionen
dc.subjectface recognitionen
dc.subjectmultiview keypoint matchingen
dc.title3D face recognition using multiview keypoint matchingen
dc.typeConference Contributionen
dc.identifier.doi10.1109/AVSS.2009.11
dc.relation.isPartOfProceedings of the Sixth IEEE International Conference on Advanced Video and Signal Based Surveillance, 2009. AVSS '09.
pubs.begin-page290
pubs.elements-id18662
pubs.end-page295
pubs.finish-date2009-09-04
pubs.place-of-publicationWashington, DC, USA
pubs.start-date2009-09-02


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