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Cross-view gait recognition through ensemble learning

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Abstract

Gait has been well known as an unobtrusive promising biometric to identify a person from a distance. However, the effectiveness of silhouette-based approaches in gait recognition is diluted due to variations of view angles. In this paper, we put forward a novel and effective method of gait recognition: cross-view gait recognition based on ensemble learning. The proposed method greatly enhances the effectiveness and reduces the sensitivity of gait recognition under various view angles conditions. Furthermore, in this paper we will introduce a novel algorithm based on ensemble learning for combining several gait learners together, which utilizes a well-designed gait feature based on area average distance. Through experimental evaluations on the well-known CASIA gait database and OU-ISIR gait database, our paper demonstrates the advantages of the proposed method in comparison with others. The contribution of this research work is to resolve the multiview angles problem of gait recognition through assembling several gait learners.

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References

  1. Sarkar S, Phillips P, Liu Z (2005) The humanID gait challenge problem: data sets, performance, and analysis. IEEE Trans Pattern Anal Mach Intell 27(02):162–177

    Google Scholar 

  2. Wu Z, Huang Y, Wang L, Wang X, Tan T (2017) A comprehensive study on cross-view gait based human identification with deep CNNs. IEEE Trans Pattern Anal Mach Intell 39(02):209–226

    Google Scholar 

  3. Zhang J, Pu J, Chen C, Fleischer R (2010) Low-resolution gait recognition. IEEE Trans Syst Man Cybern Part B (Cybernetics) 40(4):986–996

    Google Scholar 

  4. Guan Y, Li C, Roli F (2015) On reducing the effect of covariate factors in gait recognition: a classifier ensemble method. IEEE Trans Pattern Anal Mach Intell 37(07):1521–1529

    Google Scholar 

  5. Yu S, Tan D, Tan T (2006) A framework for evaluating the effect of view angle, clothing and carrying condition on gait recognition. In: International conference on pattern recognition, pp 441–444

  6. Iwama H, Okumura M, Makihara Y, Yagi Y (2012) The OU-ISIR gait database comprising the large population dataset and performance evaluation of gait recognition. IEEE Trans Inf Forensics Secur 7(5):1511–1521

    Google Scholar 

  7. Moustakas Konstantinos, Tzovaras Dimitrios, Stavropoulos Georgios (2010) Gait recognition using geometric features and soft biometrics. IEEE Signal Process Lett 17(04):367–370

    Google Scholar 

  8. Huang X, Boulgouris N (2012) Gait recognition with shifted energy image and structural feature extraction. IEEE Trans Image Process 21(04):2256–2268

    MathSciNet  MATH  Google Scholar 

  9. Kusakunniran W, Wu Q, Zhang J, Li H (2012) Gait recognition across various walking speeds using higher order shape configuration based on a differential composition model. IEEE Trans Syst Man Cybern Part B (Cybernetics) 42(6):1654–1668

    Google Scholar 

  10. Rida I, Jiang X, Marcialis GL (2016) Human body part selection by group lasso of motion for model-free gait recognition. IEEE Signal Process Lett 23(01):154–159

    Google Scholar 

  11. Theekhanont P, Kurutach W, Miguet S (2012) Gait recognition using GEI and pattern trace transform. In: International Symposium on Information Technologies in Medicine and Education, pp 936–940

  12. Wang X, Wang J, Yan K (2018) Gait recognition based on Gabor wavelets and (2D)2PCA. Multimed Tools Appl 77(10):12545–12561

    Google Scholar 

  13. Zhang J, Pu J, Chen C, Fleischer R (2010) Low-resolution gait recognition. IEEE Trans Syst Man Cybern Part B Cybern 40(04):986–997

    Google Scholar 

  14. Lai Z, Xu Y, Jin Z, Zhang D (2014) Human gait recognition via sparse discriminant projection learning. IEEE Trans Circuits Syst Video Technol 24(10):1651–1662

    Google Scholar 

  15. Muramatsu D, Makihara Y, Yagi Y (2015) Cross-view gait recognition by fusion of multiple transformation consistency measures. IET Biom 4(2):62–73

    Google Scholar 

  16. Muramatsu D, Shiraishi A, Makihara Y, Uddin MZ, Yagi Y (2015) Gait-based person recognition using arbitrary view transformation model. IEEE Trans Image Process 24(1):140–154

    MathSciNet  MATH  Google Scholar 

  17. Choudhury Sruti Das, Tjahjadi Tardi (2015) Robust view-invariant multiscale gait recognition. Pattern Recognit 48(03):798–811

    Google Scholar 

  18. Hu M, Wang Y, Zhang Z, Zhang D, Little JJ (2013) Incremental learning for video-based gait recognition with LBP flow. IEEE Trans Cybern 43(1):77–89

    Google Scholar 

  19. Connie T, Goh M, Teoh A (2017) A grassmannian approach to address view change problem in gait recognition. IEEE Trans Cybern 47(06):1395–1408

    Google Scholar 

  20. DaigoMuramatsu Makihara, Yagi Yasushi (2016) View transformation model incorporating quality measures for cross-view gait recognition. IEEE Trans Cybern 47(07):1602–1615

    Google Scholar 

  21. Zhang C, Liu W, Ma H, Fu H (2016) Siamese neural network based gait recognition for human identification. In: IEEE international conference on acoustics, speech and signal processing, pp 2832–2836

  22. Boulgouris N, Huang X (2013) Gait recognition using HMMs and dual discriminative observations for sub-dynamics analysis. IEEE Trans Image Process 22(09):3636–3647

    Google Scholar 

  23. Islam M, Islam M, Hossain M, Ferworn A, Molla M (2017) Subband entropy-based features for clothing invariant human gait recognition. Adv Robot 31(10):519–530

    Google Scholar 

  24. Aggarwal H, Vishwakarma D (2017) Covariate conscious approach for gait recognition based upon zernike moment invariants. IEEE Trans Cogn Dev Syst 1(99):1–1

    Google Scholar 

  25. Wang Liang, Ning Huazhong, Tan Tieniu, Weiming Hu (2004) Fusion of static and dynamic body biometrics for gait recognition. IEEE Trans Circuits Syst Video Technol 14(2):149–158

    Google Scholar 

  26. Cuntoor N, Kale A, Chellappa R (2003) Combining multiple evidences for gait recognition. In: IEEE international conference on acoustics, speech, and signal processing, vol 3, p III–33

  27. Veres GV, Nixon MS, Middleton L, Carter JN (2005) Fusion of dynamic and static features for gait recognition over time. In: The 7th international conference on information fusion, vol 2, pp 7–16

  28. Wang Liang, Tan Tieniu, Ning Huazhong, Weiming Hu (2003) Silhouette analysis-based gait recognition for human identification. IEEE Trans Pattern Anal Mach Intell 25(12):1505–1518

    Google Scholar 

  29. LeCun Y, Bengio Y, Hinton G (2015) Deep learning. Nature 521(5):436–445

    Google Scholar 

  30. Zhang H, Ji Y, Huang W, Liu L (2018) Sitcom-star-based clothing retrieval for video advertising: a deep learning framework. Neural Comput Appl 1(1):1–20

    Google Scholar 

  31. Takemura N, Makihara Y, Muramatsu D, Echigo T, Yagi Y (2018) On input/output architectures for convolutional neural network-based cross-view gait recognition. IEEE Trans Circuits Syst Video Technol 28(1):316–322

    Google Scholar 

  32. Kuncheva LI, Bezdek JC, Duin RPW (2001) Decision templates for multiple classifier fusion: an experimental comparison. Pattern Recognit 34(2):299–314

    MATH  Google Scholar 

  33. Wang Xuan, Wu Qingxiang, Lin Xiaojin, Zhuo Zhigiang, Huang Liuping (2016) Pedestrian identification based on fusion of multiple features and multiple classifiers. Neurocomputing 188(SI):151–159

    Google Scholar 

  34. Nguyen Tien Thanh, Nguyen Thi Thu Thuy, Pham Xuan Cuong, Liew Alan Wee-Chung (2016) A novel combining classifier method based on variational inference. Pattern Recognit 49:198–212

    Google Scholar 

  35. Dempster AP, Laird NM, Rubin DB (1977) Maximum likelihood from incomplete data via the EM algorithm. R Stat Soc 39(1):1–22

    MathSciNet  MATH  Google Scholar 

  36. Epaillard Elise, Bouguila Nizar (2016) Proportional data modeling with hidden Markov models based on generalized Dirichlet and Beta-Liouville mixtures applied to anomaly detection in public areas. Pattern Recognit 55:125–136

    Google Scholar 

  37. Zhang Q, Xu S (2009) Gait-based recognition of human using an embedded hidden Markov models. In: International conference on information engineering and computer science, pp 1–4

  38. Shakhnarovich G, Darrell T (2002) On probabilistic combination of face and gait cues for identification. In: Proceedings of fifth IEEE international conference on automatic face gesture recognition, pp 176–181

  39. Zhang H, Liu G, Chow TWS, Liu W (2011) Textual and visual content-based anti-phishing: a bayesian approach. IEEE Trans Neural Netw 22(10):1532–1546

    Google Scholar 

  40. McLachlan G, Krishnan T (2008) The EM algorithm and extensions. Wiley, New York

    MATH  Google Scholar 

  41. Zheng S, Zhang J, Huang K, He R, Tan T (2011) Robust view transformation model for gait recognition. In: The 18th IEEE international conference on image processing, pp 2073–2076

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Acknowledgements

We thank reviewers for their constructive suggestions and valuable comments.

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Authors and Affiliations

  1. China Jiliang University, No. 258, Xueyuan Street, Hangzhou, 310018, China

    Xiuhui Wang

  2. Auckland University of Technology, No. 2-14, Wakefiled Street, Auckland, 1010, New Zealand

    Wei Qi Yan

Authors
  1. Xiuhui Wang
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  2. Wei Qi Yan
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Correspondence to Xiuhui Wang.

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We declare that we have no financial and personal relationships with other people or organizations that can inappropriately influence our work, there is no professional or other personal interest of any nature or kind in any product, service and/or company that could be construed as influencing the position presented in, or the review of, the manuscript entitled “Cross-View Gait Recognition Through Ensemble Learning”.

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This work was supported in part by the National Natural Science Foundation of China (NSFC) under Grant Nos. 61303146 and 61602431 as well as a scholarship from the China Scholarship Council (CSC).

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Cite this article

Wang, X., Yan, W.Q. Cross-view gait recognition through ensemble learning. Neural Comput & Applic 32, 7275–7287 (2020). https://doi.org/10.1007/s00521-019-04256-z

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  • DOI: https://doi.org/10.1007/s00521-019-04256-z

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