On Low-Resolution Face Recognition in the Wild: Comparisons and New Techniques

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dc.contributor.authorLi, P.
dc.contributor.authorPrieto Hurtado, Loreto
dc.contributor.authorMery Quiroz, Domingo
dc.contributor.authorFlynn, P.J.
dc.date.accessioned2022-05-18T14:04:49Z
dc.date.available2022-05-18T14:04:49Z
dc.date.issued2019
dc.description.abstractAlthough face recognition systems have achieved impressive performance in recent years, the low-resolution face recognition task remains challenging, especially when the low-resolution faces are captured under non-ideal conditions, which is widely prevalent in surveillance-based applications. Faces captured in such conditions are often contaminated by blur, non-uniform lighting, and non-frontal face pose. In this paper, we analyze the face recognition techniques using data captured under low-quality conditions in the wild. We provide a comprehensive analysis of the experimental results for two of the most important applications in real surveillance applications, and demonstrate practical approaches to handle both cases that show promising performance. The following three contributions are made: (i) we conduct experiments to evaluate super-resolution methods for low-resolution face recognition; (ii) we study face re-identification on various public face datasets, including real surveillance and low-resolution subsets of large-scale datasets, presenting a baseline result for several deep learning-based approaches, and improve them by introducing a generative adversarial network pre-training approach and fully convolutional architecture; and (iii) we explore the low-resolution face identification by employing a state-of-the-art supervised discriminative learning approach. The evaluations are conducted on challenging portions of the SCface and UCCSface datasets.
dc.fuente.origenIEEE
dc.identifier.doi10.1109/TIFS.2018.2890812
dc.identifier.eissn1556-6021
dc.identifier.issn1556-6013
dc.identifier.urihttps://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=8600370
dc.identifier.urihttps://doi.org/10.1109/TIFS.2018.2890812
dc.identifier.urihttps://repositorio.uc.cl/handle/11534/64108
dc.information.autorucEscuela de ingeniería ; Prieto Hurtado, Loreto ; S/I ; 213648
dc.information.autorucEscuela de ingeniería ; Mery Quiroz, Domingo ; S/I ; 102382
dc.issue.numero8
dc.language.isoen
dc.nota.accesoContenido parcial
dc.pagina.final2012
dc.pagina.inicio2010
dc.publisherIEEE
dc.revistaIEEE Transactions on Information Forensics and Security
dc.rightsacceso restringido
dc.subjectFace
dc.subjectFace recognition
dc.subjectSurveillance
dc.subjectImage resolution
dc.subjectTask analysis
dc.subjectDeep learning
dc.subjectFeature extraction
dc.titleOn Low-Resolution Face Recognition in the Wild: Comparisons and New Techniqueses_ES
dc.typeartículo
dc.volumen14
sipa.codpersvinculados213648
sipa.codpersvinculados102382
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