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DC Field | Value | Language |
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dc.contributor.author | Alonso-Fernandez, Fernando | |
dc.contributor.author | Farrugia, Reuben A. | |
dc.contributor.author | Bigun, Josef | |
dc.date.accessioned | 2017-11-21T09:11:21Z | |
dc.date.available | 2017-11-21T09:11:21Z | |
dc.date.issued | 2017 | |
dc.identifier.citation | Alonso-Frenandez, F., Farrugia, R. A., & Bigun, J. (2017). Iris super-resolution using iterative neighbor embedding. Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), Honolulu. 655-663. | en_GB |
dc.identifier.uri | https://www.um.edu.mt/library/oar//handle/123456789/24026 | |
dc.description.abstract | Iris recognition research is heading towards enabling more relaxed acquisition conditions. This has effects on the quality and resolution of acquired images, severely affecting the accuracy of recognition systems if not tackled appropriately. In this paper, we evaluate a super-resolution algorithm used to reconstruct iris images based on iterative neighbor embedding of local image patches which tries to represent input low-resolution patches while preserving the geometry of the original high-resolution space. To this end, the geometry of the low-and high-resolution manifolds are jointly considered during the reconstruction process. We validate the system with a database of 1,872 near-infrared iris images, while fusion of two iris comparators has been adopted to improve recognition performance. The presented approach is substantially superior to bilinear/bicubic interpolations at very low resolutions, and it also outperforms a previous PCA-based iris reconstruction approach which only considers the geometry of the low-resolution manifold during the reconstruction process. | en_GB |
dc.language.iso | en | en_GB |
dc.publisher | Institute of Electrical and Electronics Engineers Inc. | en_GB |
dc.rights | info:eu-repo/semantics/restrictedAccess | en_GB |
dc.subject | Biometric identification -- Technological innovation | en_GB |
dc.subject | Image reconstruction | en_GB |
dc.title | Iris super-resolution using iterative neighbor embedding | en_GB |
dc.type | conferenceObject | en_GB |
dc.rights.holder | The 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.conferencename | Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) | en_GB |
dc.bibliographicCitation.conferenceplace | Honolulu, USA, 21-26/07/2017 | en_GB |
dc.description.reviewed | peer-reviewed | en_GB |
dc.identifier.doi | 10.1109/CVPRW.2017.94 | |
Appears in Collections: | Scholarly Works - FacICTCCE |
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