Skip to main content

Advertisement

Springer Nature Link
Log in

Semi-supervised person re-identification by similarity-embedded cycle GANs

  • Original Article
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

Abstract

Recently, person re-identification (PR-ID) has attracted numerous of research interest because of its broad applications. However, most of the existing PR-ID models always follow the supervised framework, which requires substantial labeled data. In fact, it is often very hard to get enough labeled training samples in many practical application scenarios. To overcome this limitation, some semi-supervised PR-ID methods have been presented more recently. Although some of these semi-supervised models achieve satisfied results, there is still much space to improve. In this paper, we propose a novel semi-supervised PR-ID by similarity-embedded cycle GANs (SECGAN). Our SECGAN model can learn cross-view features with limited labeled data by using cycle GANs. Simultaneously, to further enhance the ability of cycle GANs so that it can extract more discriminative and robust features, similarity metric subnet and specific features extracting subnet are embedded into cycle GANs. Extensive experiments have been conducted on three public PR-ID benchmark datasets, and the experimental results show that our proposed SECGAN approach outperforms several typical supervised methods and the existing state-of-the-art semi-supervised methods including traditional and deep learning semi-supervised methods.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Similar content being viewed by others

Explore related subjects

Discover the latest articles and news from researchers in related subjects, suggested using machine learning.

References

  1. Liao S, Li SZ (2015) Efficient PSD constrained asymmetric metric learning for person re-identification. In: IEEE international conference on computer vision, pp 3685–3693

  2. Shen Y, Lin W, Yan J, Xu M, Wu J, Wang J (2015) Person re-identification with correspondence structure learning. In: IEEE international conference on computer vision, pp 3200–3208

  3. Yang Y, Yang J, Yan J, Liao S, Yi D, Li SZ (2014) Salient color names for person re-identification. In: 13th European computer vision conference, pp 536–551

  4. Ma B, Su Y, Jurie F (2014) Covariance descriptor based on bio-inspired features for person re-identification and face verification. Image Vis Comput 32(6–7):379–390

    Article  Google Scholar 

  5. Wu Y, Lin Y, Dong X, Yan Y, Ouyang W, Yang Y (2018) Exploit the unknown gradually: one-shot video-based person re-identification by stepwise learning. In: IEEE conference on computer vision and pattern recognition, pp 5177–5186

  6. Wu Z, Li Y, Radke RJ (2015) Viewpoint invariant human re-identification in camera networks using pose priors and subject-discriminative features. IEEE Trans Pattern Anal Mach Intell 37(5):1095–1108

    Article  Google Scholar 

  7. Ma L, Yang X, Tao D (2014) Person re-identification over camera networks using multi-task distance metric learning. IEEE Trans Image Process 23(8):3656–3670

    Article  MathSciNet  Google Scholar 

  8. Ahmed E, Jones MJ, Marks TK (2015) An improved deep learning architecture for person re-identification. In: IEEE conference on computer vision and pattern, pp 3908–3916

  9. Xiao T, Li H, Ouyang W, Wang X (2016) Learning deep feature representations with domain guided dropout for person re-identification. In: IEEE conference on computer vision and pattern recognition, pp 1249–1258

  10. Li A, Liu L, Wang K, Liu S, Yan S (2015) Clothing attributes assisted person reidentification. IEEE Trans Circuits Syst Video Technol 25(5):869–878

    Article  Google Scholar 

  11. Zhao R, Ouyang W, Wang X (2013) Person re-identification by salience matching. In: IEEE International conference on computer vision, pp 2528–2535

  12. Zhao R, Ouyang W, Wang X (2014) Learning mid-level filters for person re-identification. In: IEEE conference on computer vision and pattern recognition, pp 144–151

  13. Luo Y, Liu T, Tao D, Xu C (2014) Decomposition-based transfer distance metric learning for image classification. IEEE Trans Image Process 23(9):3789–3801

    Article  MathSciNet  Google Scholar 

  14. Zhong G, Zheng Y, Li S, Fu Y (2016) Scalable large margin online metric learning. In: International joint conference on neural networks, pp 2252–2259

  15. Wang F, Zuo W, Lin L, Zhang D, Zhang L (2016) Joint learning of single-image and cross-image representations for person re-identification. In: IEEE conference on computer vision and pattern recognition, pp 1288–1296

  16. Varior RR, Haloi M, Wang G (2016) Gated siamese convolutional neural network architecture for human re-identification. In: European conference on computer vision, pp 791–808

  17. Wang J, Song Y, Leung T, Rosenberg C, Wang J, Philbin J, Chen B, Wu Y (2014) Learning fine-grained image similarity with deep ranking. In: IEEE conference on computer vision and pattern recognition 2014, pp 1386–1393

  18. Li J, Ma AJ, Yuen PC (2018) Semi-supervised region metric learning for person re-identification. Int J Comput Vis 126(8):855–874

    Article  Google Scholar 

  19. Zhu X, Jing X, Yang L, You X, Chen D, Gao G, Wang Y (2018) Semi-supervised cross-view projection-based dictionary learning for video-based person re-identification. IEEE Trans Circuits Syst Video Technol 28(10):2599–2611

    Article  Google Scholar 

  20. Liu W, Chang X, Chen L, Yang Y (2018) Semi-supervised bayesian attribute learning for person re-identification. In: Proceedings of the thirty-second conference on artificial intelligence, pp 7162–7169

  21. Xin X, Wang J, Xie R, Zhou S, Huang W, Zheng N (2019) Semi-supervised person re-identification using multi-view clustering. Pattern Recognit 88:285–297

    Article  Google Scholar 

  22. Zheng W, Gong S, Xiang T (2011) Person re-identification by probabilistic relative distance comparison. In: The 24th IEEE conference on computer vision and pattern recognition, pp 649–656

  23. Liao S, Hu Y, Zhu X, Li SZ (2015) Person re-identification by local maximal occurrence representation and metric learning. In: IEEE conference on computer vision and pattern recognition, pp 2197–2206

  24. Dong H, Lu P, Zhong S, Liu C, Ji Y, Gong S (2018) Person re-identification by enhanced local maximal occurrence representation and generalized similarity metric learning. Neurocomputing 307:25–37

    Article  Google Scholar 

  25. Lisanti G, Masi I, Bagdanov AD, Bimbo AD (2015) Person re-identification by iterative re-weighted sparse ranking. IEEE Trans Pattern Anal Mach Intell 37(8):1629–1642

    Article  Google Scholar 

  26. Zhao R, Ouyang W, Wang X (2013) Unsupervised salience learning for person re-identification. In: IEEE conference on computer vision and pattern recognition, pp 3586–3593

  27. Yu H, Wu A, Zheng W (2017) Cross-view asymmetric metric learning for unsupervised person re-identification. In: IEEE international conference on computer vision, pp 994–1002

  28. Ye M, Ma AJ, Zheng L, Li J, Yuen PC (2017) Dynamic label graph matching for unsupervised video re-identification. In: IEEE international conference on computer vision, pp 5152–5160

  29. Kviatkovsky I, Adam A, Rivlin E (2013) Color invariants for person reidentification. IEEE Trans Pattern Anal Mach Intell 35(7):1622–1634

    Article  Google Scholar 

  30. Gao Y, Ma J, Yuille AL (2017) Semi-supervised sparse representation based classification for face recognition with insufficient labeled samples. IEEE Trans Image Process 26(5):2545–2560

    Article  MathSciNet  Google Scholar 

  31. Müller E, Otto C, Ewerth R (2016) Semi-supervised identification of rarely appearing persons in video by correcting weak labels. In: International conference on multimedia retrieval, pp 381–384

  32. Bartz C, Yang H, Meinel C (2018) SEE: towards semi-supervised end-to-end scene text recognition. In: Proceedings of the thirty-second AAAI conference on artificial intelligence, pp 6674–6681

  33. Chen F, Chai J, Ren D, Liu X, Yang Y (2017) Semi-supervised distance metric learning for person re-identification. In: 16th IEEE/ACIS international conference on computer and information science, pp 733–738

  34. Chu C, Zhmoginov A, Sandler M, Cyclegan, a master of steganography, CoRR. arXiv:1712.02950

  35. Mao X, Li Q, Xie H, Lau RYK, Wang Z, Smolley SP (2017) Least squares generative adversarial networks. In: IEEE international conference on computer vision, ICCV 2017, Venice, Italy, Oct 22–29, 2017, pp. 2813–2821

  36. Mao X, Li Q, Xie H, Lau RYK, Wang Z, Smolley SP (2019) On the effectiveness of least squares generative adversarial networks. IEEE Trans Pattern Anal Mach Intell 41(12):2947–2960

    Article  Google Scholar 

  37. Zhang H, Sun Y, Liu L, Wang X, Li L, Liu W (2018) Clothingout: a category-supervised gan model for clothing segmentation and retrieval. In: Neural computing and applications, 1–12

  38. Zhong Z, Zheng L, Zheng Z, Li S, Yang Y (2018) Camera style adaptation for person re-identification. In: 2018 IEEE conference on computer vision and pattern recognition, CVPR 2018, Salt Lake City, UT, USA, June 18–22, pp 5157–5166

  39. Liu X, Tan H, Tong X, Cao J, Zhou J (2019) Feature preserving GAN and multi-scale feature enhancement for domain adaption person re-identification. Neurocomputing 364:108–118

    Article  Google Scholar 

  40. Wei L, Zhang S, Gao W, Tian Q (2018) Person transfer GAN to bridge domain gap for person re-identification. In: 2018 IEEE conference on computer vision and pattern recognition, CVPR 2018, Salt Lake City, UT, USA, June 18–22, 2018, pp 79–88

  41. Zhou S, Ke M, Luo P (2019) Multi-camera transfer GAN for person re-identification. J Vis Commun Image Represent 59:393–400

    Article  Google Scholar 

  42. Zhu X, Jing X, Wu F, Zheng W, Hu R, Xiao C, Liang C (2016) Distance learning by treating negative samples differently and exploiting impostors with symmetric triplet constraint for person re-identification. In: IEEE international conference on multimedia and expo, pp 1–6

  43. Gray D, Brennan S, Tao H (2007) Evaluating appearance models for recognition, reacquisition, and tracking. In: IEEE international workshop on performance evaluation for tracking and surveillance, vol 3, no 5, pp 1–7

  44. Zheng W, Gong S, Xiang T (2009) Associating groups of people. In: British machine vision conference, pp 1–11

  45. Zheng L, Shen L, Tian L, Wang S, Wang J, Tian Q (2015) Scalable person re-identification: a benchmark. In: IEEE international conference on computer vision, pp 1116–1124

  46. Köstinger M, Hirzer M, Wohlhart P, Roth PM, Bischof H (2012) Large scale metric learning from equivalence constraints. In: IEEE conference on computer vision and pattern recognition, pp 2288–2295

  47. Zheng W, Gong S, Xiang T (2013) Reidentification by relative distance comparison. IEEE Trans Pattern Anal Mach Intell 35(3):653–668

    Article  Google Scholar 

  48. Ma AJ, Li P (2014) Semi-supervised ranking for re-identification with few labeled image pairs. In: 12th Asian conference on computer vision, pp 598–613

  49. Su C, Zhang S, Xing J, Gao W, Tian Q (2016) Deep attributes driven multi-camera person re-identification. In: 14th European conference on computer vision, pp 475–491

  50. Wang J, Zhu X, Gong S, Li W (2018) Transferable joint attribute-identity deep learning for unsupervised person re-identification. In: IEEE conference on computer vision and pattern recognition, pp 2275–2284

  51. Abadi M, Barham P, Chen J, Chen Z, Davis A, Dean J, Devin M, Ghemawat S, Irving G, Isard M, Kudlur M, Levenberg J, Monga R, Moore S, Murray DG, Steiner B, Tucker PA, Vasudevan V, Warden P, Wicke M, Yu Y, Zheng X (2016) Tensorflow: a system for large-scale machine learning. In: 12th USENIX symposium on operating systems design and implementation, pp 265–283

  52. Schroff F, Kalenichenko D, Philbin J (2015) Facenet: A unified embedding for face recognition and clustering. In: IEEE conference on computer vision and pattern recognition, pp 815–823

Download references

Acknowledgements

The authors would like to thank the editor, the associate editor and anonymous reviewers for their constructive comments in helping improve our work. This work was supported by the NSFC-Key Project under Grant No. 61933013, the NSFC-Key Project of General Technology Fundamental Research United Fund under Grant No. U1736211, the Key Project of Natural Science Foundation of Hubei Province under Grant No. 2018CFA024, the Natural Science Foundation of Guangdong Province under Grant No. 2019A1515011076 and the Innovation Group of Guangdong Education Department under Grant No. 2018KCXTD019.

Author information

Authors and Affiliations

  1. School of Computer Science, Wuhan University, Wuhan, China

    Xinyu Zhang & Xiao-Yuan Jing

  2. School of Computer and Information Engineering, Henan University, Kaifeng, China

    Xiaoke Zhu

  3. School of Computer, Pingdingshan University, Pingdingshan, China

    Fei Ma

Authors
  1. Xinyu Zhang
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Xiao-Yuan Jing
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. Xiaoke Zhu
    View author publications

    You can also search for this author inPubMed Google Scholar

  4. Fei Ma
    View author publications

    You can also search for this author inPubMed Google Scholar

Corresponding author

Correspondence to Xiao-Yuan Jing.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zhang, X., Jing, XY., Zhu, X. et al. Semi-supervised person re-identification by similarity-embedded cycle GANs. Neural Comput & Applic 32, 14143–14152 (2020). https://doi.org/10.1007/s00521-020-04809-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-020-04809-7

Keywords