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Graph-optimized coupled discriminant projections for cross-view gait recognition

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Abstract

Graph-Embedding is a widely used learning technique in pattern recognition. However, it is difficult to construct an inter-view graph for cross-view gait samples. To remedy it, in this paper, we propose a novel cross-view gait recognition algorithm named Graph-optimized Coupled Discriminant Projections (GoCDP), which seeks coupled projections based on adaptive graph learning. Regarding the embedding graphs as variables rather than predefined constants, we integrate inter-view graph construction with projection optimization process into a unified framework. By an alternate iteration algorithm, we can ultimately obtain the optimal coupled projections. Moreover, we extend GoCDP to multi-view case called Graph-optimized Multiview Discriminant Projections (GoMDP) for multi-view subspace learning. Experimental results on two benchmark gait datasets, CASIA-B and OU-ISIR, demonstrate the effectiveness of the proposed methods.

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References

  1. Bashir K, Xiang T, Gong S (2010) Cross-view gait recognition using correlation strength. pp 1–11

  2. Bashir K, Xiang T, Gong S (2010) Gait recognition using gait entropy image. In: International conference on crime detection and prevention

  3. Ben X, Gong C, Zhang P, Jia X, Wu Q, Meng W (2019) Coupled patch alignment for matching cross-view gaits. IEEE Trans Image Process PP(6):1–1

    MathSciNet  MATH  Google Scholar 

  4. Ben X, Gong C, Zhang P, Yan R, Wu Q, Meng W (2020) Coupled bilinear discriminant projection for cross-view gait recognition. IEEE Trans Circ Syst Vid Technol :734–747

  5. Chen X, Yang TQ (2014) Cross-view gait recognition in 3-d space based on gravity center track. Appl Mech Mater :4210–4215

  6. Han J, Bhanu B (2006) Individual recognition using gait energy image. IEEE Trans Pattern Anal Mach Intell 28(2):316–322

    Article  Google Scholar 

  7. He X, Niyogi P (2003) Locality preserving projections. pp 153–160

  8. He Y, Zhang J, Shan H, Wang L (2019) Multi-task gans for view-specific feature learning in gait recognition. IEEE Trans Inf Forensics and Secur 14(1):102–113

    Article  Google Scholar 

  9. Hu M, Wang Y, Zhang Z, Little JJ, Huang D (2013) View-invariant discriminative projection for multi-view gait-based human identification. IEEE Trans Inf Forensics Secur 8(12):2034–2045

    Article  Google Scholar 

  10. 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

    Article  Google Scholar 

  11. Jean F, Bergevin R, Albu AB (2009) Computing and evaluating view-normalized body part trajectories. Image Vis Comput 27(9):1272–1284

    Article  Google Scholar 

  12. Jun K, Lee DW, Lee K, Lee S, Kim MS (2020) Feature extraction using an rnn autoencoder for skeleton-based abnormal gait recognition. IEEE Access PP(99):1–1

    Google Scholar 

  13. Kusakunniran W, Wu Q, Zhang J, Li H (2012) Gait recognition under various viewing angles based on correlated motion regression. IEEE Trans Circ Syst Vid Technol 22:966–980. https://doi.org/10.1109/TCSVT.2012.2186744

    Article  Google Scholar 

  14. Lam THW, Cheung K, Liu JNK (2011) Gait flow image. Pattern Recogn 44(4):973–987

    Article  Google Scholar 

  15. Liao R, Yu S, An W, Huang Y (2020) A model-based gait recognition method with body pose and human prior knowledge. Pattern Recogn 98:107069

    Article  Google Scholar 

  16. Luo J, Tang J, Tjahjadi T, Xiao X (2016) Robust arbitrary view gait recognition based on parametric 3d human body reconstruction and virtual posture synthesis. Pattern Recogn 60:361–377

    Article  Google Scholar 

  17. Makihara Y, Sagawa R, Mukaigawa Y, Echigo T, Yagi Y (2006) Gait recognition using a view transformation model in the frequency domain. pp 151–163

  18. Makihara Y, Suzuki A, Muramatsu D, Li X, Yagi Y (2017) Joint intensity and spatial metric learning for robust gait recognition. In: Computer vision and pattern recognition

  19. Mansur A, Makihara Y, Muramatsu D, Yagi Y (2014) Cross-view gait recognition using view-dependent discriminative analysis. In: IEEE International joint conference on biometrics

  20. Raducanu B, Dornaika F (2012) A supervised non-linear dimensionality reduction approach for manifold learning. Pattern Recogn 45(6):2432–2444

    Article  Google Scholar 

  21. Rida I, Almaadeed N, Almaadeed S (2019) Robust gait recognition: a comprehensive survey. IET Biom

  22. Takemura N, Makihara Y, Muramatsu D, Echigo T, Yagi Y (2019) On input/output architectures for convolutional neural network-based cross-view gait recognition. IEEE Trans Circ Syst Vid Technol 29(9):2708–2719

    Article  Google Scholar 

  23. Wang C, Zhang J, Pu J, Yuan X, Wang L (2010) Chrono-gait image: a novel temporal template for gait recognition. pp 257–270

  24. Wang H, Yang Y, Liu B, Fujita H (2018) A study of graph-based system for multi-view clustering. Knowl-Based Syst 163

  25. Wang X, Chen RC, Yan F, Zeng ZQ, Hong CQ (2019) Fast adaptive k-means subspace clustering for high-dimensional data. IEEE Access :42639–42651

  26. Wang X, Chen RC, Zeng ZQ, Hong CQ, Yan F (2018) Robust dimension reduction for clustering with local adaptive learning. IEEE Trans Neural Netw Learn Syst PP:1–13

    Google Scholar 

  27. 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(2):209–226

    Article  Google Scholar 

  28. Xing X, Wang K, Yan T, Lv Z (2016) Complete canonical correlation analysis with application to multi-view gait recognition. Pattern Recogn :107–117

  29. Xu W, Luo C, Ji A, Zhu C (2017) Coupled locality preserving projections for cross-view gait recognition. Neurocomputing 224:37–44

    Article  Google Scholar 

  30. Yan S, Xu D, Zhang B, Zhang H, Yang Q, Lin S (2007) Graph embedding and extensions: A general framework for dimensionality reduction. IEEE Trans Pattern Anal Mach Intell 29(1):40–51

    Article  Google Scholar 

  31. Yang W, Zhang S, Liang W (2008) A graph based subspace semi-supervised learning framework for dimensionality reduction. pp 664–677

  32. Yiwei H, Junping Z, Hongming S, Liang W (2018) Multi-task gans for view-specific feature learning in gait recognition. IEEE Trans Inf Forensics Secur PP:1–1

    Google Scholar 

  33. Yu S, Chen H, Reyes EBG, Poh N (2017) Gaitgan: Invariant gait feature extraction using generative adversarial networks. pp 532–539

  34. Yu S, Chen H, Wang Q, Shen L, Huang Y (2017) Invariant feature extraction for gait recognition using only one uniform model. Neurocomputing 239:81–93

    Article  Google Scholar 

  35. Yu S, Tan D, Tan T (2006) A framework for evaluating the effect of view angle, clothing and carrying condition on gait recognition. In: 18th International Conference on Pattern Recognition (ICPR 2006), 20-24 August, 2006, Hong Kong, China

  36. Zhang L, Qiao L, Chen S (2010) Graph-optimized locality preserving projections. Pattern Recogn 43(6):1993–2002

    Article  Google Scholar 

  37. Zhang X, Yang Y, Li T, Zhang Y, Fujita H (2020) Cmc: A consensus multi-view clustering model for predicting alzheimer’s disease progression. Comput Methods Programs Biomed :105895. https://doi.org/10.1016/j.cmpb.2020.105895

  38. Zhang Y, Huang Y, Wang L, Yu S (2019) A comprehensive study on gait biometrics using a joint cnn-based method. Pattern Recognit

  39. Zhang Y, Yang Y, Li T, Fujita H (2018) A multitask multiview clustering algorithm in heterogeneous situations based on lle and le. Knowl-Based Syst 163

  40. Zhang Z, Tran L, Liu F, Liu X (2020) On learning disentangled representations for gait recognition. IEEE Trans Pattern Anal Mach Intell

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Acknowledgements

This work is jointly supported by National Natural Science Foundation of China (61906163, 11871417) and Natural Science Foundation of the Jiangsu Higher Education Institutions of China (19KJB520018).

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  1. Yancheng Teachers University, Yancheng, 224002, China

    Wanjiang Xu

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  1. Wanjiang Xu
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Correspondence to Wanjiang Xu.

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Xu, W. Graph-optimized coupled discriminant projections for cross-view gait recognition. Appl Intell 51, 8149–8161 (2021). https://doi.org/10.1007/s10489-021-02322-5

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