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dc.contributor.authorRajnoha, Martincs
dc.contributor.authorMezina, Anzhelikacs
dc.contributor.authorBurget, Radimcs
dc.date.accessioned2020-11-02T11:55:29Z
dc.date.available2020-11-02T11:55:29Z
dc.date.issued2020-10-16cs
dc.identifier.citationApplied Sciences - Basel. 2020, vol. 10, issue 20, p. 1-27.en
dc.identifier.issn2076-3417cs
dc.identifier.other165621cs
dc.identifier.urihttp://hdl.handle.net/11012/195588
dc.description.abstractForensically trained facial reviewers are still considered as one of the most accurate approaches for person identification from video records. The human brain can utilize information, not just from a single image, but also from a sequence of images (i.e., videos), and even in the case of low-quality records or a long distance from a camera, it can accurately identify a given person. Unfortunately, in many cases, a single still image is needed. An example of such a case is a police search that is about to be announced in newspapers. This paper introduces a face database obtained from real environment counting in 17,426 sequences of images. The dataset includes persons of various races and ages and also different environments, different lighting conditions or camera device types. This paper also introduces a new multi-frame face super-resolution method and compares this method with the state-of-the-art single-frame and multi-frame super-resolution methods. We prove that the proposed method increases the quality of face images, even in cases of low-resolution low-quality input images, and provides better results than single-frame approaches that are still considered the best in this area. Quality of face images was evaluated using several objective mathematical methods, and also subjective ones, by several volunteers. The source code and the dataset were released and the experiment is fully reproducible.en
dc.formattextcs
dc.format.extent1-27cs
dc.format.mimetypeapplication/pdfcs
dc.language.isoencs
dc.publisherMDPIcs
dc.relation.ispartofApplied Sciences - Baselcs
dc.relation.urihttps://www.mdpi.com/2076-3417/10/20/7213cs
dc.rightsCreative Commons Attribution 4.0 Internationalcs
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/cs
dc.subjectface recognitionen
dc.subjectsuper resolutionen
dc.subjectmulti frameen
dc.subjectimage processingen
dc.subjectdatabaseen
dc.subjectdataseten
dc.subjectsequencesen
dc.subjectdeep learningen
dc.titleMulti-Frame Labeled Faces Database: Towards Face Super-Resolution from Realistic Video Sequencesen
thesis.grantorVysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií. Ústav telekomunikacícs
sync.item.dbidVAV-165621en
sync.item.dbtypeVAVen
sync.item.insts2021.03.01 12:52:40en
sync.item.modts2021.03.01 12:14:04en
dc.coverage.issue20cs
dc.coverage.volume10cs
dc.identifier.doi10.3390/app10207213cs
dc.rights.accessopenAccesscs
dc.rights.sherpahttp://www.sherpa.ac.uk/romeo/issn/2076-3417/cs
dc.type.driverarticleen
dc.type.statusPeer-revieweden
dc.type.versionpublishedVersionen


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Creative Commons Attribution 4.0 International
Except where otherwise noted, this item's license is described as Creative Commons Attribution 4.0 International