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Articles

Multi-stream part-fused graph convolutional networks for skeleton-based gait recognition

ORCID Icon, , , &
Pages 652-669 | Received 06 Sep 2021, Accepted 30 Dec 2021, Published online: 17 Jan 2022

References

  • An, W., Liao, R., Yu, S., Huang, Y., & Yuen, P. C. (2018). Improving Gait Recognition with 3D Pose Estimation. In J. Zhou, Y. Wang, Z. Sun, Z. Jia, J. Feng, S. Shan, K. Ubul, & Z. Guo (Eds.), Biometric Recognition. CCBR 2018. Lecture Notes in Computer Science (Vol. 10996). Springer, Cham.
  • Andersson, V. O., & Araujo, R. M. (2015). Person Identification Using Anthropometric and Gait Data from Kinect Sensor. In Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence (pp. 425–431). AAAI Press.
  • Bruna, J., Zaremba, W., Szlam, A., & LeCun, Y. (2013). Spectral networks and locally connected networks on graphs. Preprint arXiv:1312.6203.
  • Cao, C., Liu, X., Yang, Y., Yu, Y., Wang, J., Wang, Z., Huang, Y., Wang, L., Huang, C., Xu, W., & Ramanan, D. (2015). Look and Think Twice: Capturing Top-Down Visual Attention with Feedback Convolutional Neural Networks. In 2015 IEEE International Conference on Computer Vision (ICCV) (pp. 2956–2964). IEEE.
  • Cao, Z., Hidalgo, G., Simon, T., Wei, S. E., & Sheikh, Y. (2019). OpenPose: Realtime multi-person 2D pose estimation using part affinity fields. IEEE Transactions on Pattern Analysis and Machine Intelligence, 43(1), 172–186. https://doi.org/10.1109/TPAMI.34
  • Chao, H., He, Y., Zhang, J., & Feng, J. (2019). Gaitset: Regarding gait as a set for cross-view gait recognition. In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 33, pp. 8126–8133). AAAI.
  • Chen, W. S., Ge, X., & Pan, B. (2021). A novel general kernel-based non-negative matrix factorisation approach for face recognition. Connection Science, 1–26. https://doi.org/10.1080/09540091.2021.1988904
  • Defferrard, M., Bresson, X., & Vandergheynst, P. (2016). Convolutional neural networks on graphs with fast localized spectral filtering. In D. Lee, M. Sugiyama, U. Luxburg, I. Guyon, & R. Garnett (Eds.), Advances in Neural Information Processing Systems (Vol. 29, pp. 3844–3852). MIT Press.
  • Fan, C., Peng, Y., Cao, C., Liu, X., Hou, S., Chi, J., Huang, Y., Li, Q., & He, Z. (2020). Gaitpart: Temporal part-based model for gait recognition. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 14225–14233). IEEE.
  • Feng, Y., Li, Y., & Luo, J. (2016). Learning effective gait features using LSTM. In 2016 23rd International Conference on Pattern Recognition (ICPR) (pp. 325–330). IEEE.
  • Gupta, R., & Sehgal, P. (2020). HsIrisNet: Histogram based Iris recognition to allay replay and template attack using deep learning perspective. Pattern Recognition and Image Analysis, 30(4), 786–794. https://doi.org/10.1134/S105466182004015X
  • Han, J., & Bhanu, B. (2005). Individual recognition using gait energy image. IEEE Transactions on Pattern Analysis and Machine Intelligence, 28(2), 316–322. https://doi.org/10.1109/TPAMI.34
  • Hu, J., Shen, L., & Sun, G. (2018). Squeeze-and-excitation networks. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 7132–7141). IEEE.
  • Kastaniotis, D., Theodorakopoulos, I., & Fotopoulos, S. (2016). Pose-based gait recognition with local gradient descriptors and hierarchically aggregated residuals. Journal of Electronic Imaging, 25(6), 063019. https://doi.org/10.1117/1.JEI.25.6.063019
  • Kipf, T. N., & Welling, M. (2016). Semi-supervised classification with graph convolutional networks. Preprint arXiv:1609.02907.
  • Li, N., Zhao, X., & Ma, C. (2020). JointsGait: A model-based gait recognition method based on gait graph convolutional networks and joints relationship pyramid mapping. Preprint arXiv:2005.08625.
  • Liao, R., Cao, C., Garcia, E. B., Yu, S., & Huang, Y. (2017). Pose-based temporal-spatial network (PTSN) for gait recognition with carrying and clothing variations. In J. Zhou, Y. Wang, Z. Sun, Y. Xu, L. Shen, J. Feng, S. Shan, Y. Qiao, Z. Guo, & S. Yu (Eds.), Biometric Recognition. CCBR 2017 (pp. 474–483). Springer, Cham.
  • Liao, R., Yu, S., An, W., & Huang, Y. (2020). A model-based gait recognition method with body pose and human prior knowledge. Pattern Recognition, 98(2), 107069. https://doi.org/10.1016/j.patcog.2019.107069
  • Liu, F., Liu, G., Zhao, Q., & Shen, L. (2020). Robust and high-security fingerprint recognition system using optical coherence tomography. Neurocomputing, 402(8), 14–28. https://doi.org/10.1016/j.neucom.2020.03.102
  • Liu, Z., Zhang, H., Chen, Z., Wang, Z., & Ouyang, W. (2020). Disentangling and unifying graph convolutions for skeleton-based action recognition. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 143–152). IEEE.
  • Lu, T. C. (2021). CNN convolutional layer optimisation based on quantum evolutionary algorithm. Connection Science, 33(3), 482–494. https://doi.org/10.1080/09540091.2020.1841111
  • Nixon, M. S., Carter, J. N., Cunado, D., Huang, P. S., & Stevenage, S. (1996). Automatic gait recognition. In A. K. Jain, R. Bolle, & S. Pankanti (Eds.), Biometrics (pp. 231–249). Springer.
  • Shopon, M., Bari, A., & Gavrilova, M. L. (2021). Residual connection-based graph convolutional neural networks for gait recognition. The Visual Computer, 37(9), 2713–2724. https://doi.org/10.1007/s00371-021-02245-9
  • Srivastava, V., & Biswas, B. (2020). CNN-based salient features in HSI image semantic target prediction. Connection Science, 32(2), 113–131. https://doi.org/10.1080/09540091.2019.1650330
  • Tanawongsuwan, R., & Bobick, A. (2001). Gait recognition from time-normalized joint-angle trajectories in the walking plane. In Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Vol. 2, pp. II–II). IEEE.
  • Teepe, T., Khan, A., Gilg, J., Herzog, F., Hörmann, S., & Rigoll, G. (2021). GaitGraph: Graph convolutional network for skeleton-based gait recognition. Preprint arXiv:2101.11228.
  • Wang, L., Ning, H., Tan, T., & Hu, W. (2004). Fusion of static and dynamic body biometrics for gait recognition. IEEE Transactions on Circuits and Systems for Video Technology, 14(2), 149–158. https://doi.org/10.1109/TCSVT.2003.821972
  • Wu, S., Liu, Y., Zou, Z., & Weng, T. H. (2021). S_I_LSTM: Stock price prediction based on multiple data sources and sentiment analysis. Connection Science, 1–19. https://doi.org/10.1080/09540091.2021.1940101
  • Wu, Z., Huang, Y., Wang, L., Wang, X., & Tan, T. (2016). A comprehensive study on cross-view gait based human identification with deep CNNs. IEEE Transactions on Pattern Analysis and Machine Intelligence, 39(2), 209–226. https://doi.org/10.1109/TPAMI.2016.2545669
  • Yan, S., Xiong, Y., & Lin, D. (2018). Spatial temporal graph convolutional networks for skeleton-based action recognition. In Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence. AAAI Press.
  • Yang, K., Dou, Y., Lv, S., Zhang, F., & Lv, Q. (2016). Relative distance features for gait recognition with Kinect. Journal of Visual Communication and Image Representation, 39(3), 209–217. https://doi.org/10.1016/j.jvcir.2016.05.020
  • Ying, L., Qian Nan, Z., Fu Ping, W., Tuan Kiang, C., Keng Pang, L., Heng Chang, Z., Lu, C., Jun, L. G., & Nam, L. (2021). Adaptive weights learning in CNN feature fusion for crime scene investigation image classification. Connection Science, 33(3), 719–734. https://doi.org/10.1080/09540091.2021.1875987
  • Yu, S., Chen, H., Garcia Reyes, E. B., & Poh, N. (2017). Gaitgan: Invariant gait feature extraction using generative adversarial networks. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops (pp. 30–37). IEEE.
  • Yu, S., Chen, H., Wang, Q., Shen, L., & Huang, Y. (2017). Invariant feature extraction for gait recognition using only one uniform model. Neurocomputing, 239(2), 81–93. https://doi.org/10.1016/j.neucom.2017.02.006
  • 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'06) (Vol. 4, pp. 441–444). IEEE.
  • Zhang, S., Yu, H., & Zhu, G. (2021). An emotional classification method of Chinese short comment text based on ELECTRA. Connection Science, 1–20. https://doi.org/10.1080/09540091.2021.1985968
  • Zhang, Y., Huang, Y., Wang, L., & Yu, S. (2019). A comprehensive study on gait biometrics using a joint CNN-based method. Pattern Recognition, 93(3), 228–236. https://doi.org/10.1016/j.patcog.2019.04.023