A Coupled Hidden Markov Random Field Model for Simultaneous Face Clustering and Tracking in Videos

Handle URI:
http://hdl.handle.net/10754/621254
Title:
A Coupled Hidden Markov Random Field Model for Simultaneous Face Clustering and Tracking in Videos
Authors:
Wu, Baoyuan ( 0000-0003-2183-5990 ) ; Hu, Bao-Gang; Ji, Qiang
Abstract:
Face clustering and face tracking are two areas of active research in automatic facial video processing. They, however, have long been studied separately, despite the inherent link between them. In this paper, we propose to perform simultaneous face clustering and face tracking from real world videos. The motivation for the proposed research is that face clustering and face tracking can provide useful information and constraints to each other, thus can bootstrap and improve the performances of each other. To this end, we introduce a Coupled Hidden Markov Random Field (CHMRF) to simultaneously model face clustering, face tracking, and their interactions. We provide an effective algorithm based on constrained clustering and optimal tracking for the joint optimization of cluster labels and face tracking. We demonstrate significant improvements over state-of-the-art results in face clustering and tracking on several videos.
KAUST Department:
Visual Computing Center (VCC)
Citation:
Wu B, Hu B-G, Ji Q (2016) A Coupled Hidden Markov Random Field Model for Simultaneous Face Clustering and Tracking in Videos. Pattern Recognition. Available: http://dx.doi.org/10.1016/j.patcog.2016.10.022.
Publisher:
Elsevier BV
Journal:
Pattern Recognition
Issue Date:
25-Oct-2016
DOI:
10.1016/j.patcog.2016.10.022
Type:
Article
ISSN:
0031-3203
Sponsors:
The work was completed when the first author was a visiting student at Rensselaer Polytechnic Institute (RPI), supported by a scholarship from China Scholarship Council (CSC). We thank CSC and RPI for their supports. Qiang Ji is supported in part by a grant from the US National Science Foundation (NSF, No. 1145152). Bao-Gang Hu and Baoyuan Wu are supported in part by the National Natural Science Foundation of China (NSFC, No. 61273196 and 61573348). We greatly thank Professor Siwei Lyu for his constructive comments to this work.
Additional Links:
http://www.sciencedirect.com/science/article/pii/S0031320316303387
Appears in Collections:
Articles; Visual Computing Center (VCC)

Full metadata record

DC FieldValue Language
dc.contributor.authorWu, Baoyuanen
dc.contributor.authorHu, Bao-Gangen
dc.contributor.authorJi, Qiangen
dc.date.accessioned2016-10-31T07:37:15Z-
dc.date.available2016-10-31T07:37:15Z-
dc.date.issued2016-10-25en
dc.identifier.citationWu B, Hu B-G, Ji Q (2016) A Coupled Hidden Markov Random Field Model for Simultaneous Face Clustering and Tracking in Videos. Pattern Recognition. Available: http://dx.doi.org/10.1016/j.patcog.2016.10.022.en
dc.identifier.issn0031-3203en
dc.identifier.doi10.1016/j.patcog.2016.10.022en
dc.identifier.urihttp://hdl.handle.net/10754/621254-
dc.description.abstractFace clustering and face tracking are two areas of active research in automatic facial video processing. They, however, have long been studied separately, despite the inherent link between them. In this paper, we propose to perform simultaneous face clustering and face tracking from real world videos. The motivation for the proposed research is that face clustering and face tracking can provide useful information and constraints to each other, thus can bootstrap and improve the performances of each other. To this end, we introduce a Coupled Hidden Markov Random Field (CHMRF) to simultaneously model face clustering, face tracking, and their interactions. We provide an effective algorithm based on constrained clustering and optimal tracking for the joint optimization of cluster labels and face tracking. We demonstrate significant improvements over state-of-the-art results in face clustering and tracking on several videos.en
dc.description.sponsorshipThe work was completed when the first author was a visiting student at Rensselaer Polytechnic Institute (RPI), supported by a scholarship from China Scholarship Council (CSC). We thank CSC and RPI for their supports. Qiang Ji is supported in part by a grant from the US National Science Foundation (NSF, No. 1145152). Bao-Gang Hu and Baoyuan Wu are supported in part by the National Natural Science Foundation of China (NSFC, No. 61273196 and 61573348). We greatly thank Professor Siwei Lyu for his constructive comments to this work.en
dc.publisherElsevier BVen
dc.relation.urlhttp://www.sciencedirect.com/science/article/pii/S0031320316303387en
dc.rights© 2016. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/en
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/en
dc.subjectFace clusteringen
dc.subjectFace trackingen
dc.subjectCoupled Hidden Markov Random Fielden
dc.titleA Coupled Hidden Markov Random Field Model for Simultaneous Face Clustering and Tracking in Videosen
dc.typeArticleen
dc.contributor.departmentVisual Computing Center (VCC)en
dc.identifier.journalPattern Recognitionen
dc.eprint.versionPost-printen
dc.contributor.institutionNational Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, P.R. China, 100190en
dc.contributor.institutionDepartment of Electrical, Computer, and Systems Engineering, Rensselaer Polytechnic Institute, Troy, NY, USA, 12180en
kaust.authorWu, Baoyuanen
kaust.authorHu, Bao-Gangen
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