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dc.contributor.authorWong, Yongkang
dc.contributor.authorSanderson, Conrad
dc.contributor.authorMau, Sandra
dc.contributor.authorLovell, Brian C
dc.date.accessioned2021-01-17T23:02:01Z
dc.date.available2021-01-17T23:02:01Z
dc.date.issued2010
dc.identifier.isbn9781424475421en_US
dc.identifier.doi10.1109/icpr.2010.299en_US
dc.identifier.urihttp://hdl.handle.net/10072/401178
dc.description.abstractWhile existing face recognition systems based on local features are robust to issues such as misalignment, they can exhibit accuracy degradation when comparing images of differing resolutions. This is common in surveillance environments where a gallery of high resolution mugshots is compared to low resolution CCTV probe images, or where the size of a given image is not a reliable indicator of the underlying resolution (e.g. poor optics). To alleviate this degradation, we propose a compensation framework which dynamically chooses the most appropriate face recognition system for a given pair of image resolutions. This framework applies a novel resolution detection method which does not rely on the size of the input images, but instead exploits the sensitivity of local features to resolution using a probabilistic multi-region histogram approach. Experiments on a resolution-modified version of the "Labeled Faces in the Wild" dataset show that the proposed resolution detector frontend obtains a 99% average accuracy in selecting the most appropriate face recognition system, resulting in higher overall face discrimination accuracy (across several resolutions) compared to the individual baseline face recognition systems.en_US
dc.publisherIEEEen_US
dc.relation.ispartofconferencename2010 20th International Conference on Pattern Recognition (ICPR)en_US
dc.relation.ispartofconferencetitle2010 20th International Conference on Pattern Recognitionen_US
dc.relation.ispartofdatefrom2010-08-23
dc.relation.ispartofdateto2010-08-26
dc.relation.ispartoflocationIstanbul, Turkeyen_US
dc.relation.ispartofpagefrom1204en_US
dc.relation.ispartofpageto1207en_US
dc.titleDynamic Amelioration of Resolution Mismatches for Local Feature Based Identity Inferenceen_US
dc.typeConference outputen_US
dcterms.bibliographicCitationWong, Y; Sanderson, C; Mau, S; Lovell, BC, Dynamic Amelioration of Resolution Mismatches for Local Feature Based Identity Inference, 2010 20th International Conference on Pattern Recognition, 2010, pp. 1200-41207en_US
dc.date.updated2021-01-17T22:58:32Z
dc.description.versionAccepted Manuscript (AM)en_US
gro.rights.copyright© 2010 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.en_US
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gro.griffith.authorSanderson, Conrad


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