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Super-Resolution for Selfie Biometrics: Introduction and Application to Face and Iris
Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).ORCID-id: 0000-0002-1400-346X
University of Malta, Msida, Malta.ORCID-id: 0000-0001-8106-9891
Universidad Autonoma de Madrid, Madrid, Spain.ORCID-id: 0000-0002-6343-5656
Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).ORCID-id: 0000-0002-4929-1262
2019 (engelsk)Inngår i: Selfie Biometrics: Advances and Challenges / [ed] Ajita Rattani, Reza Derakhshani & Arun A. Ross, Cham: Springer, 2019, 1, s. 105-128Kapittel i bok, del av antologi (Fagfellevurdert)
Abstract [en]

Biometric research is heading towards enabling more relaxed acquisition conditions. This has effects on the quality and resolution of acquired images, severly affecting the accuracy of recognition systems if not tackled appropriately. In this chapter, we give an overview of recent research in super-resolution reconstruction applied to biometrics, with a focus on face and iris images in the visible spectrum, two prevalent modalities in selfie biometrics. After an introduction to the generic topic of super-resolution, we investigate methods adapted to cater for the particularities of these two modalities. By experiments, we show the benefits of incorporating super-resolution to improve the quality of biometric images prior to recognition. © Springer Nature AG 2019

sted, utgiver, år, opplag, sider
Cham: Springer, 2019, 1. s. 105-128
Serie
Advances in Computer Vision and Pattern Recognition, ISSN 2191-6586, E-ISSN 2191-6594 ; 77
HSV kategori
Identifikatorer
URN: urn:nbn:se:hh:diva-38508DOI: 10.1007/978-3-030-26972-2_5Scopus ID: 2-s2.0-85073171533Libris ID: z8pbp2xpw3p1fc6vISBN: 978-3-030-26971-5 (tryckt)ISBN: 978-3-030-26972-2 (digital)OAI: oai:DiVA.org:hh-38508DiVA, id: diva2:1268697
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Swedish Research CouncilVinnovaKnowledge Foundation
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Other funder: CogniMetrics (TEC2015-70627-R) from MINECO/FEDER

Tilgjengelig fra: 2018-12-06 Laget: 2018-12-06 Sist oppdatert: 2020-02-03bibliografisk kontrollert

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