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dc.contributor.authorChen, Jiaweien_US
dc.contributor.authorKonrad, Januszen_US
dc.contributor.authorIshwar, Prakashen_US
dc.coverage.spatialSalt Lake City, UTen_US
dc.date.accessioned2019-11-13T16:10:53Z
dc.date.available2019-11-13T16:10:53Z
dc.date.issued2018-01-01
dc.identifierhttp://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000457636800200&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=6e74115fe3da270499c3d65c9b17d654
dc.identifier.citationJiawei Chen, Janusz Konrad, Prakash Ishwar. 2018. "VGAN-Based Image Representation Learning for Privacy-Preserving Facial Expression Recognition." PROCEEDINGS 2018 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION WORKSHOPS (CVPRW). IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Salt Lake City, UT, 2018-06-18 - 2018-06-22. https://doi.org/10.1109/CVPRW.2018.00207
dc.identifier.issn2160-7508
dc.identifier.urihttps://hdl.handle.net/2144/38496
dc.description.abstractReliable facial expression recognition plays a critical role in human-machine interactions. However, most of the facial expression analysis methodologies proposed to date pay little or no attention to the protection of a user's privacy. In this paper, we propose a Privacy-Preserving Representation-Learning Variational Generative Adversarial Network (PPRL-VGAN) to learn an image representation that is explicitly disentangled from the identity information. At the same time, this representation is discriminative from the standpoint of facial expression recognition and generative as it allows expression-equivalent face image synthesis. We evaluate the proposed model on two public datasets under various threat scenarios. Quantitative and qualitative results demonstrate that our approach strikes a balance between the preservation of privacy and data utility. We further demonstrate that our model can be effectively applied to other tasks such as expression morphing and image completion.en_US
dc.format.extent1651 - 1660 (10)en_US
dc.languageEnglish
dc.publisherIEEEen_US
dc.relation.ispartofPROCEEDINGS 2018 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION WORKSHOPS (CVPRW)
dc.subjectComputer science, artificial intelligenceen_US
dc.subjectComputer scienceen_US
dc.subjectPattern recognitionen_US
dc.subjectFacial expression recognitionen_US
dc.titleVGAN-based image representation learning for privacy-preserving facial expression recognitionen_US
dc.typeConference materialsen_US
dc.description.versionAccepted manuscripten_US
dc.identifier.doi10.1109/CVPRW.2018.00207
pubs.elements-sourceweb-of-scienceen_US
pubs.notesEmbargo: Not knownen_US
pubs.organisational-groupBoston Universityen_US
pubs.organisational-groupBoston University, College of Engineeringen_US
pubs.organisational-groupBoston University, College of Engineering, Department of Electrical & Computer Engineeringen_US
pubs.publication-statusPublisheden_US
dc.identifier.mycv359244


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