short-paper

Multi-cue Augmented Face Clustering

Authors:

Changqing Zhang,

Xiaochun CaoAuthors Info & Claims

MM '15: Proceedings of the 23rd ACM international conference on Multimedia

Pages 1095 - 1098

https://doi.org/10.1145/2733373.2806290

Published: 13 October 2015 Publication History

Abstract

Face clustering is an important but challenging task since facial images always have huge variation due to change in facial expressions, head poses and partial occlusions, etc. Moreover, face clustering is actually an unsupervised problem which makes it more difficult to reach an accurate result. Fortunately, there are some cues that can be used to improve clustering performance. In this paper, two types of cues are employed. The first one is pairwise constraints: must-link and cannot-link constraints, which can be extracted from the temporal and spatial knowledge of data. The other is that each face is associated with a series of attributes (i.e, gender) which can contribute discrimination among faces. To take advantage of the above cues, we propose a new algorithm, Multi-cue Augmented Face Clustering (McAFC), which effectively incorporates the cues via graph-guided sparse subspace clustering technique. Specially, facial images from the same individual are encouraged to be connected while faces from different persons are restrained to be connected. Experiments on three face datasets from real-world videos show the improvements of our algorithm over the state-of-the-art methods.

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Cited By

Walawalkar DGarrido P(2024)VideoClusterNet: Self-supervised and Adaptive Face Clustering for VideosComputer Vision – ECCV 202410.1007/978-3-031-73404-5_22(377-396)Online publication date: 30-Oct-2024
https://doi.org/10.1007/978-3-031-73404-5_22
Ding JFang XJia LJiang YLi R(2023)Diversity Multi-View Clustering With Subspace and NMF-Based Manifold LearningIEEE Access10.1109/ACCESS.2023.326483711(37041-37051)Online publication date: 2023
https://doi.org/10.1109/ACCESS.2023.3264837
Sun MZhang PWang SZhou STu WLiu XZhu EWang CShen HZhuang YSmith JYang YCesar PMetze FPrabhakaran B(2021)Scalable Multi-view Subspace Clustering with Unified AnchorsProceedings of the 29th ACM International Conference on Multimedia10.1145/3474085.3475516(3528-3536)Online publication date: 17-Oct-2021
https://dl.acm.org/doi/10.1145/3474085.3475516
Show More Cited By

Index Terms

Multi-cue Augmented Face Clustering
1. Applied computing

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Information

Published In

cover image ACM Conferences

MM '15: Proceedings of the 23rd ACM international conference on Multimedia

October 2015

1402 pages

ISBN:9781450334594

DOI:10.1145/2733373

General Chairs:
Xiaofang Zhou
The University of Queensland, Australia
,
Alan F. Smeaton
Dublin City University, Ireland
,
Qi Tian
The University of Texas at San Antonio, USA
,
Program Chairs:
Dick C.A. Bulterman
FXPAL, USA
,
Heng Tao Shen
The University of Queensland, Australia
,
Ketan Mayer-Patel
The University of North Carolina, USA
,
Shuicheng Yan
National University of Singapore, Singapore

Copyright © 2015 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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SIGMM: ACM Special Interest Group on Multimedia

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 13 October 2015

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Short-paper

Funding Sources

Chinese Academy of Sciences
National Natural Science Foundation of China

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MM '15

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SIGMM

MM '15: ACM Multimedia Conference

October 26 - 30, 2015

Brisbane, Australia

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MM '15 Paper Acceptance Rate 56 of 252 submissions, 22%;

Overall Acceptance Rate 2,145 of 8,556 submissions, 25%

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Citations

Cited By

Walawalkar DGarrido P(2024)VideoClusterNet: Self-supervised and Adaptive Face Clustering for VideosComputer Vision – ECCV 202410.1007/978-3-031-73404-5_22(377-396)Online publication date: 30-Oct-2024
https://doi.org/10.1007/978-3-031-73404-5_22
Ding JFang XJia LJiang YLi R(2023)Diversity Multi-View Clustering With Subspace and NMF-Based Manifold LearningIEEE Access10.1109/ACCESS.2023.326483711(37041-37051)Online publication date: 2023
https://doi.org/10.1109/ACCESS.2023.3264837
Sun MZhang PWang SZhou STu WLiu XZhu EWang CShen HZhuang YSmith JYang YCesar PMetze FPrabhakaran B(2021)Scalable Multi-view Subspace Clustering with Unified AnchorsProceedings of the 29th ACM International Conference on Multimedia10.1145/3474085.3475516(3528-3536)Online publication date: 17-Oct-2021
https://dl.acm.org/doi/10.1145/3474085.3475516
Sharma VTapaswi MSarfraz MStiefelhagen R(2020)Video Face Clustering With Self-Supervised Representation LearningIEEE Transactions on Biometrics, Behavior, and Identity Science10.1109/TBIOM.2019.29472642:2(145-157)Online publication date: Apr-2020
https://doi.org/10.1109/TBIOM.2019.2947264
Sharma VTapaswi MSarfraz MStiefelhagen R(2020)Clustering based Contrastive Learning for Improving Face Representations2020 15th IEEE International Conference on Automatic Face and Gesture Recognition (FG 2020)10.1109/FG47880.2020.00011(109-116)Online publication date: Nov-2020
https://doi.org/10.1109/FG47880.2020.00011
Röthlingshöfer VSharma VStiefelhagen RAmsaleg LHuet BLarson MGravier GHung HNgo CTsang Ooi W(2019)Self-supervised Face-Grouping on GraphsProceedings of the 27th ACM International Conference on Multimedia10.1145/3343031.3351071(247-256)Online publication date: 15-Oct-2019
https://dl.acm.org/doi/10.1145/3343031.3351071
Sharma VTapaswi MSarfraz MStiefelhagen R(2019)Self-Supervised Learning of Face Representations for Video Face Clustering2019 14th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2019)10.1109/FG.2019.8756609(1-8)Online publication date: May-2019
https://doi.org/10.1109/FG.2019.8756609
Zhang SHuang JLim JGong YWang JAhuja NYang M(2019)Tracking Persons-of-Interest via Unsupervised Representation AdaptationInternational Journal of Computer Vision10.1007/s11263-019-01212-1Online publication date: 3-Sep-2019
https://doi.org/10.1007/s11263-019-01212-1
Zhang ZLuo PLoy CTang X(2016)Joint Face Representation Adaptation and Clustering in VideosComputer Vision – ECCV 201610.1007/978-3-319-46487-9_15(236-251)Online publication date: 17-Sep-2016
https://doi.org/10.1007/978-3-319-46487-9_15
Qi LShi YWang HYang WGao Y(2016)Multi-view Subspace Clustering via a Global Low-Rank Affinity MatrixIntelligent Data Engineering and Automated Learning – IDEAL 201610.1007/978-3-319-46257-8_35(321-331)Online publication date: 13-Sep-2016
https://doi.org/10.1007/978-3-319-46257-8_35
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