research-article

Collaborative Networks for Person Verification

Authors:

Jinzhuo WangAuthors Info & Claims

MuVer '17: Proceedings of the First International Workshop on Multimedia Verification

Pages 3 - 11

https://doi.org/10.1145/3132384.3132385

Published: 27 October 2017 Publication History

Abstract

This paper considers the person verification problem in video surveillance systems. The goal is to verify whether or not a given pair of human body images belong to the same identity. For this purpose, we propose a method of collaborative networks which contains two kinds of novel agents. Specifically, one is implemented by an improved siamese network (iSN) and the other is employed as a deep discriminative network (DDN). The iSN explores the commonness and difference properties of pairwise feature vectors to enhance the robustness for person verification. Instead, the DDN learns to discriminate the difference of input images from the original difference space, without individual feature extraction. Both of the networks capture the correlation of the input and determine whether they are the same or not. Moreover, we introduce a collaborative learning strategy to fuse them into a unified architecture. Extensive experiments are conducted on four person verification datasets, including CUHK01, CUHK03, PRID2011 and QMUL GRID. We obtain competitive or superior performance compared to state-of-the-art methods.

References

[1]

Ejaz Ahmed, Michael Jones, and Tim K. Marks. 2015. An improved deep learning architecture for person re-identification. In IEEE Conference on Computer Vision and Pattern Recognition. 3908--3916.

[2]

C. L. Chen, T. Xiang, and S. Gong. 2009. Multi-camera activity correlation analysis. In IEEE Computer Society Conference on Computer Vision and Pattern Recognition. 1988--1995.

[3]

D. Chen, Z. Yuan, B. Chen, and N. Zheng. 2016. Similarity Learning with Spatial Constraints for Person Re-identification. In IEEE Conference on Computer Vision and Pattern Recognition. 1268--1277.

[4]

S. Z. Chen, C. C. Guo, and J. Lai. 2016. Deep Ranking for Person Re-identification via Joint Representation Learning. IEEE Transactions on Image Processing A Publication of the IEEE Signal Processing Society 25, 5 (2016), 2353--2367.

[5]

W. Chen, X. Chen, J. Zhang, and K. Huang. 2017. A Multitask Deep Network for Person Re-identification. In Thirty-First AAAI Conference on Artificial Intelligence.

[6]

Y. C. Chen, W. S. Zheng, and J. Lai. 2015. Mirror representation for modeling view-specific transform in person re-identification. In International Conference on Artificial Intelligence. 3402-- 3408.

Digital Library

[7]

D. Cheng, Y. Gong, S. Zhou, J. Wang, and N. Zheng. 2016. Person Re-identification by Multi-Channel Parts-Based CNN with Improved Triplet Loss Function. In IEEE Conference on Computer Vision and Pattern Recognition. 1335--1344.

[8]

Ronan Collobert, Koray Kavukcuoglu, and Clment Farabet. 2011. Torch7: A Matlab-like Environment for Machine Learning. In BigLearn, NIPS Workshop.

[9]

Jason V Davis, Brian Kulis, Prateek Jain, Suvrit Sra, and Inderjit S Dhillon. 2007. Information-theoretic metric learning. In Machine Learning, Proceedings of the Twenty-Fourth International Conference. 209--216.

Digital Library

[10]

S. Ding, L. Lin, G. Wang, and H. Chao. 2015. Deep feature learning with relative distance comparison for person re-identification. Pattern Recognition 48, 10 (2015), 2993--3003.

Digital Library

[11]

Y. Dong, L. Zhen, S. Liao, and Stan Z. Li. 2014. Deep Metric Learning for Person Re-identification. In International Conference on Pattern Recognition. 34--39.

Digital Library

[12]

M. Farenzena, L. Bazzani, A. Perina, V. Murino, and M. Cristani. 2010. Person re-identification by symmetry-driven accumulation of local features. In Computer Vision and Pattern Recognition (CVPR). 2360--2367.

[13]

M. Guillaumin, J. Verbeek, and C. Schmid. 2009. Is that you? Metric learning approaches for face identification. In IEEE International Conference on Computer Vision. 498--505.

[14]

K. He, X. Zhang, S. Ren, and J. Sun. 2015. Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification. In IEEE International Conference on Computer Vision. 1026--1034.

Digital Library

[15]

Martin Hirzer. 2012. Large scale metric learning from equivalence constraints. In Computer Vision and Pattern Recognition. 2288-- 2295.

Digital Library

[16]

Martin Hirzer, Csaba Beleznai, Peter M. Roth, and Horst Bischof. 2011. Person Re-Identification by Descriptive and Discriminative Classification. In Proc. Scandinavian Conference on Image Analysis (SCIA).

Digital Library

[17]

S. Huang, J. Lu, J. Zhou, and Anil K. Jain. 2015. Nonlinear Local Metric Learning for Person Re-identification. Computer Science (2015).

[18]

Alex Krizhevsky, Ilya Sutskever, and Geoffrey E. Hinton. 2012. ImageNet classification with deep convolutional neural networks. In International Conference on Neural Information Processing Systems. 1097--1105.

Digital Library

[19]

W. Li and X. Wang. 2013. Locally Aligned Feature Transforms across Views. In Computer Vision and Pattern Recognition. 3594--3601.

Digital Library

[20]

W. Li, R. Zhao, and X. Wang. 2012. Human Reidentification with Transferred Metric Learning. In ACCV.

Digital Library

[21]

W. Li, R. Zhao, T. Xiao, and X. Wang. 2014. DeepReID: Deep Filter Pairing Neural Network for Person Re-identification. In IEEE Conference on Computer Vision and Pattern Recognition. 152--159.

Digital Library

[22]

S. Liao, Y. Hu, X. Zhu, and Stan Z. Li. 2015. Person reidentification by Local Maximal Occurrence representation and metric learning. In Computer Vision and Pattern Recognition (CVPR). 2197--2206.

[23]

S. Liao and Stan Z. Li. 2015. Efficient PSD Constrained Asymmetric Metric Learning for Person Re-Identification. In IEEE International Conference on Computer Vision. 3685--3693.

Digital Library

[24]

K. Lin, S. Wang, and J. Zhou. 2017. Collaborative Deep Reinforcement Learning. arXiv preprint arXiv:1702.05796 (2017).

[25]

J. Liu, Z. J. Zha, Q. Tian, D. Liu, T. Yao, Q. Ling, and T. Mei. 2016. Multi-Scale Triplet CNN for Person Re-Identification. In ACM on Multimedia Conference. 192--196.

Digital Library

[26]

B. Ma, Y. Su, and Frederic Jurie. 2012. Local Descriptors Encoded by Fisher Vectors for Person Re-identification. In European Conference on Computer Vision (ECCV). 413--422.

Digital Library

[27]

L. Ma, X. Yang, and D. Tao. 2014. Person Re-Identification Over Camera Networks Using Multi-Task Distance Metric Learning. IEEE Transactions on Image Processing A Publication of the IEEE Signal Processing Society 23, 8 (2014), 3656--70.

[28]

Niki Martinel, Christian Micheloni, and Gian Luca Foresti. 2015. Kernelized Saliency-Based Person Re-Identification Through Multiple Metric Learning. IEEE Transactions on Image Processing 24, 12 (2015), 5645--5658.

Digital Library

[29]

Tetsu Matsukawa, Takahiro Okabe, Einoshin Suzuki, and Yoichi Sato. 2016. Hierarchical Gaussian Descriptor for Person Reidentification. In Computer Vision and Pattern Recognition (CVPR). 1363--1372.

[30]

Sakrapee Paisitkriangkrai, Chunhua Shen, and Van Den Hengel Anton. 2015. Learning to rank in person re-identification with metric ensembles. In IEEE Conference on Computer Vision and Pattern Recognition. 1846--1855.

[31]

Karen Simonyan and Andrew Zisserman. 2014. Very Deep Convolutional Networks for Large-Scale Image Recognition. Computer Science (2014).

[32]

C. Su, S. Zhang, J. Xing, W. Gao, and Q. Tian. 2016. Deep Attributes Driven Multi-Camera Person Re-identification. arXiv preprint arXiv:1605.03259 (2016).

[33]

Arulkumar Subramaniam, Moitreya Chatterjee, and Anurag Mittal. 2016. Deep Neural Networks with Inexact Matching for Person Re-Identification. In Advances in Neural Information Processing Systems 29. 2667--2675.

[34]

D. Tao, Y. Guo, M. Song, Y. Li, Z. Yu, and Y. Y. Tang. 2016. Person Re-Identification by Dual-Regularized KISS Metric Learning. IEEE Transactions on Image Processing A Publication of the IEEE Signal Processing Society 25, 6 (2016), 2726--2738.

Digital Library

[35]

F. Wang, W. Zuo, L. Lin, D. Zhang, and L. Zhang. 2016. Joint Learning of Single-Image and Cross-Image Representations for Person Re-identification. In IEEE Conference on Computer Vision and Pattern Recognition. 1288--1296.

[36]

Jinzhuo Wang, Wenmin Wang, Ronggang Wang, Wen Gao, and others. 2016. Deep Alternative Neural Network: Exploring Contexts as Early as Possible for Action Recognition. In Advances in Neural Information Processing Systems. 811--819.

[37]

Kilian Q Weinberger and Lawrence K. Saul. 2006. Distance Metric Learning for Large Margin Nearest Neighbor Classification. In NIPS. 207--244.

Digital Library

[38]

L. Wu, Shen, C., and Anton van den Hengel. 2016. PersonNet: Person Re-identification with Deep Convolutional Neural Networks. arXiv preprint arXiv:1601.07255 (2016).

[39]

F. Xiong, M. Gou, Octavia Camps, and Mario Sznaier. 2014. Person re-identification using kernel-based metric learning methods. In European conference on computer vision. Springer, 1--16.

[40]

Y. Yang. 2014. Salient color names for person re-identification. In European Conference on Computer Vision.

[41]

Y. Yang, S. Liao, Z. Lei, and Stan Z Li. 2016. Large scale similarity learning using similar pairs for person verification. In AAAI. 3655--3661.

Digital Library

[42]

X. Zeng, W. Ouyang, J. Yan, H. Li, T. Xiao, K. Wang, Y. Liu, Y. Zhou, B. Yang, Z. Wang, H. Zhou, and X. Wang. 2016. Crafting GBD-Net for Object Detection. arXiv preprint arXiv:1610.02579 (2016).

[43]

L. Zhang, T. Xiang, and S. Gong. 2016. Learning a Discriminative Null Space for Person Re-identification. In IEEE Conference on Computer Vision and Pattern Recognition. 1239--1248.

[44]

R. Zhao, W. Ouyang, and X. Wang. 2013. Unsupervised Salience Learning for Person Re-identification. In IEEE Conference on Computer Vision and Pattern Recognition. 3586--3593.

Digital Library

[45]

R. Zhao, W. Ouyang, and X. Wang. 2014. Learning Mid-level Filters for Person Re-identification. In Computer Vision and Pattern Recognition (CVPR). 144--151.

Digital Library

Cited By

Samonte MOray ESan Juan K(2022)Exploratory Analysis of e-Government Implementations and Blockchain Technology on Document and Identity Verification of Overseas Filipino WorkersProceedings of the 2022 6th International Conference on E-Business and Internet10.1145/3572647.3572680(217-221)Online publication date: 14-Oct-2022
https://dl.acm.org/doi/10.1145/3572647.3572680
Wang JWang WGao W(2020)Fast and Accurate Action Detection in Videos With Motion-Centric Attention ModelIEEE Transactions on Circuits and Systems for Video Technology10.1109/TCSVT.2018.288706130:1(117-130)Online publication date: Jan-2020
https://doi.org/10.1109/TCSVT.2018.2887061
Mezaris VNixon LPapadopoulos SSpangenberg JLiu QLienhart RWang HChen SBoll SChen PFriedland GLi JYan S(2017)MuVer'17Proceedings of the 25th ACM international conference on Multimedia10.1145/3123266.3132058(1983-1984)Online publication date: 23-Oct-2017
https://dl.acm.org/doi/10.1145/3123266.3132058

Index Terms

Collaborative Networks for Person Verification
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision problems
        Matching
        Object identification
        Object recognition

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cover image ACM Conferences

MuVer '17: Proceedings of the First International Workshop on Multimedia Verification

October 2017

40 pages

ISBN:9781450355100

DOI:10.1145/3132384

General Chairs:
Vasileios Mezaris
Centre for Research and Technology Hellas, Greece
,
Lyndon Nixon
MODUL Technology GmbH, Austria
,
Symeon Papadopoulos
Centre for Research and Technology Hellas, Greece
,
Jochen Spangenberg
Deutsche Welle, Germany

Copyright © 2017 ACM.

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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: 27 October 2017

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Shenzhen Peacock Plan
Guangdong Science and Technology Project
Shenzhen Key Laboratory for Intelligent Multimedia and Virtual Reality

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MM '17

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SIGMM

MM '17: ACM Multimedia Conference

October 27, 2017

California, Mountain View, USA

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Cited By

Samonte MOray ESan Juan K(2022)Exploratory Analysis of e-Government Implementations and Blockchain Technology on Document and Identity Verification of Overseas Filipino WorkersProceedings of the 2022 6th International Conference on E-Business and Internet10.1145/3572647.3572680(217-221)Online publication date: 14-Oct-2022
https://dl.acm.org/doi/10.1145/3572647.3572680
Wang JWang WGao W(2020)Fast and Accurate Action Detection in Videos With Motion-Centric Attention ModelIEEE Transactions on Circuits and Systems for Video Technology10.1109/TCSVT.2018.288706130:1(117-130)Online publication date: Jan-2020
https://doi.org/10.1109/TCSVT.2018.2887061
Mezaris VNixon LPapadopoulos SSpangenberg JLiu QLienhart RWang HChen SBoll SChen PFriedland GLi JYan S(2017)MuVer'17Proceedings of the 25th ACM international conference on Multimedia10.1145/3123266.3132058(1983-1984)Online publication date: 23-Oct-2017
https://dl.acm.org/doi/10.1145/3123266.3132058

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