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Conference Paper: Facial landmark detection by deep multi-task learning

TitleFacial landmark detection by deep multi-task learning
Authors
Issue Date2014
Citation
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2014, v. 8694 LNCS, n. PART 6, p. 94-108 How to Cite?
AbstractFacial landmark detection has long been impeded by the problems of occlusion and pose variation. Instead of treating the detection task as a single and independent problem, we investigate the possibility of improving detection robustness through multi-task learning. Specifically, we wish to optimize facial landmark detection together with heterogeneous but subtly correlated tasks, e.g. head pose estimation and facial attribute inference. This is non-trivial since different tasks have different learning difficulties and convergence rates. To address this problem, we formulate a novel tasks-constrained deep model, with task-wise early stopping to facilitate learning convergence. Extensive evaluations show that the proposed task-constrained learning (i) outperforms existing methods, especially in dealing with faces with severe occlusion and pose variation, and (ii) reduces model complexity drastically compared to the state-of-the-art method based on cascaded deep model [21]. © 2014 Springer International Publishing.
Persistent Identifierhttp://hdl.handle.net/10722/273668
ISSN
2023 SCImago Journal Rankings: 0.606

 

DC FieldValueLanguage
dc.contributor.authorZhang, Zhanpeng-
dc.contributor.authorLuo, Ping-
dc.contributor.authorLoy, Chen Change-
dc.contributor.authorTang, Xiaoou-
dc.date.accessioned2019-08-12T09:56:19Z-
dc.date.available2019-08-12T09:56:19Z-
dc.date.issued2014-
dc.identifier.citationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2014, v. 8694 LNCS, n. PART 6, p. 94-108-
dc.identifier.issn0302-9743-
dc.identifier.urihttp://hdl.handle.net/10722/273668-
dc.description.abstractFacial landmark detection has long been impeded by the problems of occlusion and pose variation. Instead of treating the detection task as a single and independent problem, we investigate the possibility of improving detection robustness through multi-task learning. Specifically, we wish to optimize facial landmark detection together with heterogeneous but subtly correlated tasks, e.g. head pose estimation and facial attribute inference. This is non-trivial since different tasks have different learning difficulties and convergence rates. To address this problem, we formulate a novel tasks-constrained deep model, with task-wise early stopping to facilitate learning convergence. Extensive evaluations show that the proposed task-constrained learning (i) outperforms existing methods, especially in dealing with faces with severe occlusion and pose variation, and (ii) reduces model complexity drastically compared to the state-of-the-art method based on cascaded deep model [21]. © 2014 Springer International Publishing.-
dc.languageeng-
dc.relation.ispartofLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)-
dc.titleFacial landmark detection by deep multi-task learning-
dc.typeConference_Paper-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1007/978-3-319-10599-4_7-
dc.identifier.scopuseid_2-s2.0-84906348918-
dc.identifier.volume8694 LNCS-
dc.identifier.issuePART 6-
dc.identifier.spage94-
dc.identifier.epage108-
dc.identifier.eissn1611-3349-
dc.identifier.issnl0302-9743-

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