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Conference Paper: From facial parts responses to face detection: A deep learning approach

TitleFrom facial parts responses to face detection: A deep learning approach
Authors
Issue Date2015
Citation
Proceedings of the IEEE International Conference on Computer Vision, 2015, v. 2015 International Conference on Computer Vision, ICCV 2015, p. 3676-3684 How to Cite?
Abstract© 2015 IEEE. In this paper, we propose a novel deep convolutional network (DCN) that achieves outstanding performance on FDDB, PASCAL Face, and AFW. Specifically, our method achieves a high recall rate of 90.99% on the challenging FDDB benchmark, outperforming the state-of-the-art method [23] by a large margin of 2.91%. Importantly, we consider finding faces from a new perspective through scoring facial parts responses by their spatial structure and arrangement. The scoring mechanism is carefully formulated considering challenging cases where faces are only partially visible. This consideration allows our network to detect faces under severe occlusion and unconstrained pose variation, which are the main difficulty and bottleneck of most existing face detection approaches. We show that despite the use of DCN, our network can achieve practical runtime speed.
Persistent Identifierhttp://hdl.handle.net/10722/273719
ISSN
2023 SCImago Journal Rankings: 12.263
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorYang, Shuo-
dc.contributor.authorLuo, Ping-
dc.contributor.authorLoy, Chen Change-
dc.contributor.authorTang, Xiaoou-
dc.date.accessioned2019-08-12T09:56:27Z-
dc.date.available2019-08-12T09:56:27Z-
dc.date.issued2015-
dc.identifier.citationProceedings of the IEEE International Conference on Computer Vision, 2015, v. 2015 International Conference on Computer Vision, ICCV 2015, p. 3676-3684-
dc.identifier.issn1550-5499-
dc.identifier.urihttp://hdl.handle.net/10722/273719-
dc.description.abstract© 2015 IEEE. In this paper, we propose a novel deep convolutional network (DCN) that achieves outstanding performance on FDDB, PASCAL Face, and AFW. Specifically, our method achieves a high recall rate of 90.99% on the challenging FDDB benchmark, outperforming the state-of-the-art method [23] by a large margin of 2.91%. Importantly, we consider finding faces from a new perspective through scoring facial parts responses by their spatial structure and arrangement. The scoring mechanism is carefully formulated considering challenging cases where faces are only partially visible. This consideration allows our network to detect faces under severe occlusion and unconstrained pose variation, which are the main difficulty and bottleneck of most existing face detection approaches. We show that despite the use of DCN, our network can achieve practical runtime speed.-
dc.languageeng-
dc.relation.ispartofProceedings of the IEEE International Conference on Computer Vision-
dc.titleFrom facial parts responses to face detection: A deep learning approach-
dc.typeConference_Paper-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1109/ICCV.2015.419-
dc.identifier.scopuseid_2-s2.0-84973904792-
dc.identifier.volume2015 International Conference on Computer Vision, ICCV 2015-
dc.identifier.spage3676-
dc.identifier.epage3684-
dc.identifier.isiWOS:000380414100411-
dc.identifier.issnl1550-5499-

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