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- Publisher Website: 10.1109/TMM.2019.2902096
- Scopus: eid_2-s2.0-85071575518
- WOS: WOS:000483015200008
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Article: Facial Landmark Machines: A Backbone-Branches Architecture With Progressive Representation Learning
Title | Facial Landmark Machines: A Backbone-Branches Architecture With Progressive Representation Learning |
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Authors | |
Keywords | Face Shape Computer architecture Task analysis Face detection |
Issue Date | 2019 |
Publisher | IEEE. The Journal's web site is located at http://www.ieee.org/organizations/tab/tmm.html |
Citation | IEEE Transactions on Multimedia, 2019, v. 21 n. 9, p. 2248-2262 How to Cite? |
Abstract | Facial landmark localization plays a critical role in face recognition and analysis. In this paper, we propose a novel cascaded backbone-branches fully convolutional neural network (BB-FCN) for rapidly and accurately localizing facial landmarks in unconstrained and cluttered settings. Our proposed BB-FCN generates facial landmark response maps directly from raw images without any preprocessing. BB-FCN follows a coarse-to-fine cascaded pipeline, which consists of a backbone network to roughly detect the locations of all facial landmarks and one branch network for each type of detected landmark to further refine its location. Furthermore, to facilitate the facial landmark localization under unconstrained settings, we propose a large-scale benchmark named SYSU16K, which contains 16 000 faces with large variations in pose, expression, illumination, and resolution. Extensive experimental evaluations demonstrate that our proposed BB-FCN can significantly outperform the state of the art under both constrained (i.e., within detected facial regions only) and unconstrained settings. We further confirm that high-quality facial landmarks localized with our proposed network can also improve the precision and recall of face detection. |
Persistent Identifier | http://hdl.handle.net/10722/284235 |
ISSN | 2023 Impact Factor: 8.4 2023 SCImago Journal Rankings: 2.260 |
ISI Accession Number ID |
DC Field | Value | Language |
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dc.contributor.author | LIU, L | - |
dc.contributor.author | LI, G | - |
dc.contributor.author | XIE, Y | - |
dc.contributor.author | Yu, Y | - |
dc.contributor.author | WANG, Q | - |
dc.contributor.author | LIN, L | - |
dc.date.accessioned | 2020-07-20T05:57:08Z | - |
dc.date.available | 2020-07-20T05:57:08Z | - |
dc.date.issued | 2019 | - |
dc.identifier.citation | IEEE Transactions on Multimedia, 2019, v. 21 n. 9, p. 2248-2262 | - |
dc.identifier.issn | 1520-9210 | - |
dc.identifier.uri | http://hdl.handle.net/10722/284235 | - |
dc.description.abstract | Facial landmark localization plays a critical role in face recognition and analysis. In this paper, we propose a novel cascaded backbone-branches fully convolutional neural network (BB-FCN) for rapidly and accurately localizing facial landmarks in unconstrained and cluttered settings. Our proposed BB-FCN generates facial landmark response maps directly from raw images without any preprocessing. BB-FCN follows a coarse-to-fine cascaded pipeline, which consists of a backbone network to roughly detect the locations of all facial landmarks and one branch network for each type of detected landmark to further refine its location. Furthermore, to facilitate the facial landmark localization under unconstrained settings, we propose a large-scale benchmark named SYSU16K, which contains 16 000 faces with large variations in pose, expression, illumination, and resolution. Extensive experimental evaluations demonstrate that our proposed BB-FCN can significantly outperform the state of the art under both constrained (i.e., within detected facial regions only) and unconstrained settings. We further confirm that high-quality facial landmarks localized with our proposed network can also improve the precision and recall of face detection. | - |
dc.language | eng | - |
dc.publisher | IEEE. The Journal's web site is located at http://www.ieee.org/organizations/tab/tmm.html | - |
dc.relation.ispartof | IEEE Transactions on Multimedia | - |
dc.rights | IEEE Transactions on Multimedia. Copyright © IEEE. | - |
dc.rights | ©20xx IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. | - |
dc.subject | Face | - |
dc.subject | Shape | - |
dc.subject | Computer architecture | - |
dc.subject | Task analysis | - |
dc.subject | Face detection | - |
dc.title | Facial Landmark Machines: A Backbone-Branches Architecture With Progressive Representation Learning | - |
dc.type | Article | - |
dc.identifier.email | Yu, Y: yzyu@cs.hku.hk | - |
dc.identifier.authority | Yu, Y=rp01415 | - |
dc.description.nature | link_to_subscribed_fulltext | - |
dc.identifier.doi | 10.1109/TMM.2019.2902096 | - |
dc.identifier.scopus | eid_2-s2.0-85071575518 | - |
dc.identifier.hkuros | 310933 | - |
dc.identifier.volume | 21 | - |
dc.identifier.issue | 9 | - |
dc.identifier.spage | 2248 | - |
dc.identifier.epage | 2262 | - |
dc.identifier.isi | WOS:000483015200008 | - |
dc.publisher.place | United States | - |
dc.identifier.issnl | 1520-9210 | - |