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Conference Paper: Joint face representation adaptation and clustering in videos

TitleJoint face representation adaptation and clustering in videos
Authors
KeywordsFace clustering
Face recognition
Transfer learning
Convolutional network
Issue Date2016
Citation
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2016, v. 9907 LNCS, p. 236-251 How to Cite?
Abstract© Springer International Publishing AG 2016. Clustering faces in movies or videos is extremely challenging since characters’ appearance can vary drastically under different scenes. In addition, the various cinematic styles make it difficult to learn a universal face representation for all videos. Unlike previous methods that assume fixed handcrafted features for face clustering, in this work, we formulate a joint face representation adaptation and clustering approach in a deep learning framework. The proposed method allows face representation to gradually adapt from an external source domain to a target video domain. The adaptation of deep representation is achieved without any strong supervision but through iteratively discovered weak pairwise identity constraints derived from potentially noisy face clustering result. Experiments on three benchmark video datasets demonstrate that our approach generates character clusters with high purity compared to existing video face clustering methods, which are either based on deep face representation (without adaptation) or carefully engineered features.
Persistent Identifierhttp://hdl.handle.net/10722/273573
ISSN
2023 SCImago Journal Rankings: 0.606
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorZhang, Zhanpeng-
dc.contributor.authorLuo, Ping-
dc.contributor.authorLoy, Chen Change-
dc.contributor.authorTang, Xiaoou-
dc.date.accessioned2019-08-12T09:55:58Z-
dc.date.available2019-08-12T09:55:58Z-
dc.date.issued2016-
dc.identifier.citationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2016, v. 9907 LNCS, p. 236-251-
dc.identifier.issn0302-9743-
dc.identifier.urihttp://hdl.handle.net/10722/273573-
dc.description.abstract© Springer International Publishing AG 2016. Clustering faces in movies or videos is extremely challenging since characters’ appearance can vary drastically under different scenes. In addition, the various cinematic styles make it difficult to learn a universal face representation for all videos. Unlike previous methods that assume fixed handcrafted features for face clustering, in this work, we formulate a joint face representation adaptation and clustering approach in a deep learning framework. The proposed method allows face representation to gradually adapt from an external source domain to a target video domain. The adaptation of deep representation is achieved without any strong supervision but through iteratively discovered weak pairwise identity constraints derived from potentially noisy face clustering result. Experiments on three benchmark video datasets demonstrate that our approach generates character clusters with high purity compared to existing video face clustering methods, which are either based on deep face representation (without adaptation) or carefully engineered features.-
dc.languageeng-
dc.relation.ispartofLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)-
dc.subjectFace clustering-
dc.subjectFace recognition-
dc.subjectTransfer learning-
dc.subjectConvolutional network-
dc.titleJoint face representation adaptation and clustering in videos-
dc.typeConference_Paper-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1007/978-3-319-46487-9_15-
dc.identifier.scopuseid_2-s2.0-84990031687-
dc.identifier.volume9907 LNCS-
dc.identifier.spage236-
dc.identifier.epage251-
dc.identifier.eissn1611-3349-
dc.identifier.isiWOS:000389384800015-
dc.identifier.issnl0302-9743-

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