Skip to main content
SpringerLink
Log in
Book cover

European Conference on Computer Vision

ECCV 2020: Computer Vision – ECCV 2020 pp 382–398Cite as

Gait Lateral Network: Learning Discriminative and Compact Representations for Gait Recognition

  • Conference paper
  • First Online:

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 12354))

Abstract

Gait recognition aims at identifying different people by the walking patterns, which can be conducted at a long distance without the cooperation of subjects. A key challenge for gait recognition is to learn representations from the silhouettes that are invariant to the factors such as clothing, carrying conditions and camera viewpoints. Besides being discriminative for identification, the gait representations should also be compact for storage to keep millions of subjects registered in the gallery. In this work, we propose a novel network named Gait Lateral Network (GLN) which can learn both discriminative and compact representations from the silhouettes for gait recognition. Specifically, GLN leverages the inherent feature pyramid in deep convolutional neural networks to enhance the gait representations. The silhouette-level and set-level features extracted by different stages are merged with the lateral connections in a top-down manner. Besides, GLN is equipped with a Compact Block which can significantly reduce the dimension of the gait representations without hindering the accuracy. Extensive experiments on CASIA-B and OUMVLP show that GLN can achieve state-of-the-art performance using the 256-dimensional representations. Under the most challenging condition of walking in different clothes on CASIA-B, our method improves the rank-1 accuracy by \(6.45\%\).

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

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

Notes

  1. 1.

    The silhouette-level features have the shape of [batch, set, channel, height, width] where the set dimension denotes the number of the silhouettes in an unordered set. And the set-level features have the shape of [batch, channel, height, width].

References

  1. Ariyanto, G., Nixon, M.S.: Model-based 3D gait biometrics. In: International Joint Conference on Biometrics, pp. 1–7 (2011)

    Google Scholar 

  2. Bashir, K., Xiang, T., Gong, S., Mary, Q.: Gait representation using flow fields. In: BMVC, pp. 1–11 (2009)

    Google Scholar 

  3. Bodor, R., Drenner, A., Fehr, D., Masoud, O., Papanikolopoulos, N.: View-independent human motion classification using image-based reconstruction. Image Vis. Comput. 27(8), 1194–1206 (2009)

    Article  Google Scholar 

  4. Bouchrika, I., Goffredo, M., Carter, J., Nixon, M.: On using gait in forensic biometrics. J. Forensic Sci. 56(4), 882–889 (2011)

    Article  Google Scholar 

  5. Castro, F.M., Marín-Jiménez, M.J., Guil, N., de la Blanca, N.P.: Multimodal feature fusion for CNN-based gait recognition: an empirical comparison. Neural Comput. Appl. 32, 14173–14193 (2020). https://doi.org/10.1007/s00521-020-04811-z

    Article  Google Scholar 

  6. Chao, H., He, Y., Zhang, J., Feng, J.: Gaitset: regarding gait as a set for cross-view gait recognition. In: AAAI, vol. 33, pp. 8126–8133 (2019)

    Google Scholar 

  7. Fu, Y., et al.: Horizontal pyramid matching for person re-identification. In: AAAI, vol. 33, pp. 8295–8302 (2019)

    Google Scholar 

  8. Han, J., Bhanu, B.: Individual recognition using gait energy image. TPAMI 28(2), 316–322 (2005)

    Article  Google Scholar 

  9. Hariharan, B., Arbeláez, P., Girshick, R., Malik, J.: Hypercolumns for object segmentation and fine-grained localization. In: CVPR, pp. 447–456 (2015)

    Google Scholar 

  10. He, Y., Zhang, J., Shan, H., Wang, L.: Multi-task GANs for view-specific feature learning in gait recognition. IEEE Trans. Inf. Forensics Secur. 14(1), 102–113 (2018)

    Article  Google Scholar 

  11. Hermans, A., Beyer, L., Leibe, B.: In defense of the triplet loss for person re-identification. arXiv preprint arXiv:1703.07737 (2017)

  12. Hu, B., Gao, Y., Guan, Y., Long, Y., Lane, N., Ploetz, T.: Robust cross-view gait identification with evidence: a discriminant gait GAN (DIGGAN) approach on 10000 people. arXiv preprint arXiv:1811.10493 (2018)

  13. Ioffe, S., Szegedy, C.: Batch normalization: accelerating deep network training by reducing internal covariate shift. ICML 37, 448–456 (2015)

    Google Scholar 

  14. Ji, S., Xu, W., Yang, M., Yu, K.: 3D convolutional neural networks for human action recognition. TPAMI 35(1), 221–231 (2012)

    Article  Google Scholar 

  15. Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. In: NeurIPS, pp. 1097–1105 (2012)

    Google Scholar 

  16. Kusakunniran, W., Wu, Q., Li, H., Zhang, J.: Multiple views gait recognition using view transformation model based on optimized gait energy image. In: ICCV Workshops, pp. 1058–1064 (2009)

    Google Scholar 

  17. Kusakunniran, W., Wu, Q., Zhang, J., Ma, Y., Li, H.: A new view-invariant feature for cross-view gait recognition. IEEE Trans. Inf. Forensics Secur. 8(10), 1642–1653 (2013)

    Article  Google Scholar 

  18. Larsen, P.K., Simonsen, E.B., Lynnerup, N.: Gait analysis in forensic medicine. J. Forensic Sci. 53(5), 1149–1153 (2008)

    Article  Google Scholar 

  19. Lee, C.Y., Xie, S., Gallagher, P.W., Zhang, Z., Tu, Z.: Deeply-supervised nets. ArXiv abs/1409.5185 (2014)

    Google Scholar 

  20. Liao, R., Cao, C., Garcia, E.B., Yu, S., Huang, Y.: Pose-based temporal-spatial network (PTSN) for gait recognition with carrying and clothing variations. In: Zhou, J., et al. (eds.) CCBR 2017. LNCS, vol. 10568, pp. 474–483. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-69923-3_51

    Chapter  Google Scholar 

  21. Lin, T.Y., Dollár, P., Girshick, R., He, K., Hariharan, B., Belongie, S.: Feature pyramid networks for object detection. In: CVPR, pp. 2117–2125 (2017)

    Google Scholar 

  22. Liu, C.T., Wu, C.W., Wang, Y.C.F., Chien, S.Y.: Spatially and temporally efficient non-local attention network for video-based person re-identification. arXiv preprint arXiv:1908.01683 (2019)

  23. Liu, W., et al.: SSD: single shot multibox detector. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9905, pp. 21–37. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46448-0_2

    Chapter  Google Scholar 

  24. Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation. In: CVPR, pp. 3431–3440 (2015)

    Google Scholar 

  25. Luo, H., Gu, Y., Liao, X., Lai, S., Jiang, W.: Bag of tricks and a strong baseline for deep person re-identification. In: CVPR Workshops (2019)

    Google Scholar 

  26. Makihara, Y., Sagawa, R., Mukaigawa, Y., Echigo, T., Yagi, Y.: Gait recognition using a view transformation model in the frequency domain. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006. LNCS, vol. 3953, pp. 151–163. Springer, Heidelberg (2006). https://doi.org/10.1007/11744078_12

    Chapter  Google Scholar 

  27. Maltoni, D., Maio, D., Jain, A.K., Prabhakar, S.: Handbook of Fingerprint Recognition. Springer, London (2009). https://doi.org/10.1007/978-1-84882-254-2

    Book  MATH  Google Scholar 

  28. Mirza, M., Osindero, S.: Conditional generative adversarial nets. ArXiv abs/1411.1784 (2014)

    Google Scholar 

  29. Paszke, A., et al.: Pytorch: an imperative style, high-performance deep learning library. In: NeurIPS, pp. 8024–8035 (2019)

    Google Scholar 

  30. Ruder, S.: An overview of gradient descent optimization algorithms. arXiv preprint arXiv:1609.04747 (2016)

  31. Shiraga, K., Makihara, Y., Muramatsu, D., Echigo, T., Yagi, Y.: GeiNet: view-invariant gait recognition using a convolutional neural network. In: International Conference on Biometrics, pp. 1–8 (2016)

    Google Scholar 

  32. Srivastava, N., Hinton, G.E., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: a simple way to prevent neural networks from overfitting. J. Mach. Learn. Res. 15, 1929–1958 (2014)

    MathSciNet  MATH  Google Scholar 

  33. Sun, Y., Chen, Y., Wang, X., Tang, X.: Deep learning face representation by joint identification-verification. In: NeurIPS,pp. 1988–1996 (2014)

    Google Scholar 

  34. Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. In: CVPR, pp. 2818–2826 (2016)

    Google Scholar 

  35. Takemura, N., Makihara, Y., Muramatsu, D., Echigo, T., Yagi, Y.: On input/output architectures for convolutional neural network-based cross-view gait recognition. IEEE Trans. Circuits Syst. Video Technol. (2017)

    Google Scholar 

  36. Takemura, N., Makihara, Y., Muramatsu, D., Echigo, T., Yagi, Y.: Multi-view large population gait dataset and its performance evaluation for cross-view gait recognition. IPSJ Trans. Comput. Vis. Appl. 10(1), 1–14 (2018). https://doi.org/10.1186/s41074-018-0039-6

    Article  Google Scholar 

  37. Wang, C., Zhang, J., Wang, L., Pu, J., Yuan, X.: Human identification using temporal information preserving gait template. TPAMI 34(11), 2164–2176 (2011)

    Article  Google Scholar 

  38. Wang, L., Tan, T., Ning, H., Hu, W.: Silhouette analysis-based gait recognition for human identification. TPAMI 25(12), 1505–1518 (2003)

    Article  Google Scholar 

  39. Wildes, R.P.: Iris recognition: an emerging biometric technology. Proc. IEEE 85(9), 1348–1363 (1997)

    Article  Google Scholar 

  40. Wolf, T., Babaee, M., Rigoll, G.: Multi-view gait recognition using 3D convolutional neural networks. In: ICIP, pp. 4165–4169 (2016)

    Google Scholar 

  41. Wu, Z., Huang, Y., Wang, L., Wang, X., Tan, T.: A comprehensive study on cross-view gait based human identification with deep CNNs. TPAMI 39(2), 209–226 (2016)

    Article  Google Scholar 

  42. Yu, S., Tan, D., Tan, T.: A framework for evaluating the effect of view angle, clothing and carrying condition on gait recognition. In: International Conference on Pattern Recognition, vol. 4, pp. 441–444 (2006)

    Google Scholar 

  43. Zeiler, M.D., Fergus, R.: Visualizing and understanding convolutional networks. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8689, pp. 818–833. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-10590-1_53

    Chapter  Google Scholar 

  44. Zhang, K., Luo, W., Ma, L., Liu, W., Li, H.: Learning joint gait representation via quintuplet loss minimization. In: CVPR, pp. 4700–4709 (2019)

    Google Scholar 

  45. Zhang, Y., Huang, Y., Wang, L., Yu, S.: A comprehensive study on gait biometrics using a joint CNN-based method. Pattern Recogn. 93, 228–236 (2019)

    Article  Google Scholar 

  46. Zhang, Z., et al.: Gait recognition via disentangled representation learning. In: CVPR, pp. 4710–4719 (2019)

    Google Scholar 

  47. Zhu, W., Hu, J., Sun, G., Cao, X., Qiao, Y.: A key volume mining deep framework for action recognition. In: CVPR, pp. 1991–1999 (2016)

    Google Scholar 

Download references

Acknowledgements

We are grateful to Prof. Dongbin Zhao for his support to this work.

Author information

Authors and Affiliations

  1. Institute of Automation, Chinese Academy of Sciences, Beijing, China

    Saihui Hou & Yongzhen Huang

  2. Beijing University of Technology, Beijing, China

    Xu Liu

  3. WATRIX AI, Beijing, China

    Saihui Hou, Chunshui Cao, Xu Liu & Yongzhen Huang

Authors
  1. Saihui Hou
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Chunshui Cao
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Xu Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Yongzhen Huang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Yongzhen Huang .

Editor information

Editors and Affiliations

  1. University of Oxford, Oxford, UK

    Andrea Vedaldi

  2. Graz University of Technology, Graz, Austria

    Horst Bischof

  3. University of Freiburg, Freiburg im Breisgau, Germany

    Thomas Brox

  4. University of North Carolina at Chapel Hill, Chapel Hill, NC, USA

    Jan-Michael Frahm

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Hou, S., Cao, C., Liu, X., Huang, Y. (2020). Gait Lateral Network: Learning Discriminative and Compact Representations for Gait Recognition. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, JM. (eds) Computer Vision – ECCV 2020. ECCV 2020. Lecture Notes in Computer Science(), vol 12354. Springer, Cham. https://doi.org/10.1007/978-3-030-58545-7_22

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-58545-7_22

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-58544-0

  • Online ISBN: 978-3-030-58545-7

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation