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From attributes to faces: a conditional generative network for face genera-tion

dc.contributor.authorWang, Yaohui
dc.contributor.authorDantcheva, Antitza
dc.contributor.authorBremond, Francois
dc.contributor.editorBrömme, Arslan
dc.contributor.editorBusch, Christoph
dc.contributor.editorDantcheva, Antitza
dc.contributor.editorRathgeb, Christian
dc.contributor.editorUhl, Andreas
dc.date.accessioned2019-06-17T10:00:25Z
dc.date.available2019-06-17T10:00:25Z
dc.date.issued2018
dc.description.abstractRecent advances in computer vision have aimed at extracting and classifying auxiliary biometric information such as age, gender, as well as health attributes, referred to as soft biometrics or attributes. We here seek to explore the inverse problem, namely face generation based on attribute labels, which is of interest due to related applications in law enforcement and entertainment. Particularly, we propose a method based on deep conditional generative adversarial network (DCGAN), which introduces additional data (e.g., labels) towards determining specific representations of generated images. We present experimental results of the method, trained on the dataset CelebA, and validate these based on two GAN-quality-metrics, as well as based on three face detectors and one commercial off the shelf (COTS) attribute classifier. While these are early results, our findings indicate the method’s ability to generate realistic faces from attribute labels.en
dc.identifier.isbn978-3-88579-676-4
dc.identifier.pissn1617-5469
dc.identifier.urihttps://dl.gi.de/handle/20.500.12116/23800
dc.language.isoen
dc.publisherKöllen Druck+Verlag GmbH
dc.relation.ispartofBIOSIG 2018 - Proceedings of the 17th International Conference of the Biometrics Special Interest Group
dc.relation.ispartofseriesLecture Notes in Informatics (LNI) - Proceedings, Volume P-283
dc.subjectAttributes
dc.subjectSoft Biometrics
dc.subjectGenerative Adversarial Networks
dc.titleFrom attributes to faces: a conditional generative network for face genera-tionen
dc.typeText/Conference Paper
gi.citation.publisherPlaceBonn
gi.conference.date26.-28. September 2018
gi.conference.locationDarmstadt

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