{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,19]],"date-time":"2026-06-19T12:55:56Z","timestamp":1781873756299,"version":"3.54.5"},"reference-count":92,"publisher":"Springer Science and Business Media LLC","issue":"7","license":[{"start":{"date-parts":[[2026,6,19]],"date-time":"2026-06-19T00:00:00Z","timestamp":1781827200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,6,19]],"date-time":"2026-06-19T00:00:00Z","timestamp":1781827200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100002858","name":"China Postdoctoral Science Foundation","doi-asserted-by":"publisher","award":["2025M784248"],"award-info":[{"award-number":["2025M784248"]}],"id":[{"id":"10.13039\/501100002858","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Int J Comput Vis"],"published-print":{"date-parts":[[2026,7]]},"DOI":"10.1007\/s11263-026-02912-1","type":"journal-article","created":{"date-parts":[[2026,6,19]],"date-time":"2026-06-19T12:32:18Z","timestamp":1781872338000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["MPANet: Motion Pattern Aggregation Network for Gait Recognition"],"prefix":"10.1007","volume":"134","author":[{"given":"Kang","family":"Ma","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiao","family":"Liang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chunshui","family":"Cao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Guoren","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3998-5989","authenticated-orcid":false,"given":"Dezhi","family":"Zheng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,6,19]]},"reference":[{"key":"2912_CR1","doi-asserted-by":"crossref","unstructured":"Ariyanto, G., & Nixon, M. S. (2011). Model-based 3d gait biometrics. Int. Joi. Conf. Bio (pp. 1\u20137)","DOI":"10.1109\/IJCB.2011.6117582"},{"key":"2912_CR2","doi-asserted-by":"crossref","unstructured":"Bai, S., Ma, B., Chang, H., Huang, R., & Chen, X. (2022). Salient-to-broad transition for video person re-identification. IEEE Conf. Comput. Vis. Pattern Recog (pp. 7339\u20137348)","DOI":"10.1109\/CVPR52688.2022.00719"},{"key":"2912_CR3","unstructured":"Bobick, A.F., & Johnson, A.Y. (2001). Gait recognition using static, activity-specific parameters. In: IEEE Conf. Comput. Vis. Pattern Recog., p."},{"issue":"8","key":"2912_CR4","doi-asserted-by":"publisher","first-page":"1194","DOI":"10.1016\/j.imavis.2008.11.008","volume":"27","author":"R Bodor","year":"2009","unstructured":"Bodor, R., Drenner, A., Fehr, D., Masoud, O., & Papanikolopoulos, N. (2009). View-independent human motion classification using image-based reconstruction. Image Vision Comput., 27(8), 1194\u20131206.","journal-title":"Image Vision Comput."},{"issue":"4","key":"2912_CR5","doi-asserted-by":"publisher","first-page":"882","DOI":"10.1111\/j.1556-4029.2011.01793.x","volume":"56","author":"I Bouchrika","year":"2011","unstructured":"Bouchrika, I., Goffredo, M., Carter, J., & Nixon, M. (2011). On using gait in forensic biometrics. J. Forensic Sci., 56(4), 882\u2013889.","journal-title":"J. Forensic Sci."},{"key":"2912_CR6","doi-asserted-by":"crossref","unstructured":"Cao, Y., Xu, J., Lin, S., Wei, F., & Hu, H. (2019). Gcnet: Non-local networks meet squeeze-excitation networks and beyond. In: IEEE Int. Conf. Comput. Vis. Worksh., pp. 0\u20130.","DOI":"10.1109\/ICCVW.2019.00246"},{"key":"2912_CR7","doi-asserted-by":"crossref","unstructured":"Cao, Z., Simon, T., Wei, S.-E., & Sheikh, Y. (2017). Realtime multi-person 2d pose estimation using part affinity fields. IEEE Conf. Comput. Vis. Pattern Recog (pp. 7291\u20137299)","DOI":"10.1109\/CVPR.2017.143"},{"key":"2912_CR8","doi-asserted-by":"crossref","unstructured":"Chai, T., Li, A., Zhang, S., Li, Z., & Wang, Y. (2022). Lagrange motion analysis and view embeddings for improved gait recognition. In: IEEE Conf. Comput. Vis. Pattern Recog., pp. 20249\u201320258.","DOI":"10.1109\/CVPR52688.2022.01961"},{"key":"2912_CR9","doi-asserted-by":"crossref","unstructured":"Chao, H., He, Y., Zhang, J., & Feng, J. (2019). Gaitset: Regarding gait as a set for cross-view gait recognition (Vol. 33, pp. 8126\u20138133). AAAI.","DOI":"10.1609\/aaai.v33i01.33018126"},{"issue":"7","key":"2912_CR10","first-page":"3467","volume":"44","author":"H Chao","year":"2021","unstructured":"Chao, H., Wang, K., He, Y., Zhang, J., & Feng, J. (2021). Gaitset: Cross-view gait recognition through utilizing gait as a deep set. IEEE Trans. Pattern Anal. Mach. Intell., 44(7), 3467\u20133478.","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"2912_CR11","doi-asserted-by":"crossref","unstructured":"Chen, T., Ding, S., Xie, J., Yuan, Y., Chen, W., Yang, Y., Ren, Z., & Wang, Z. (2019). Abd-net: Attentive but diverse person re-identification. In: IEEE Int. Conf. Comput. Vis., pp. 8351\u20138361","DOI":"10.1109\/ICCV.2019.00844"},{"key":"2912_CR12","doi-asserted-by":"crossref","unstructured":"Cui, Y., & Kang, Y. (2022). Gaittransformer: Multiple-temporal-scale transformer for cross-view gait recognition. Int. Conf. Multimedia and Expo (pp. 1\u20136). IEEE.","DOI":"10.1109\/ICME52920.2022.9859928"},{"key":"2912_CR13","doi-asserted-by":"crossref","unstructured":"Cui, Y., & Kang, Y. (2022). Gaittransformer: Multiple-temporal-scale transformer for cross-view gait recognition. Int. Conf. Multimedia and Expo (pp. 1\u20136). IEEE.","DOI":"10.1109\/ICME52920.2022.9859928"},{"key":"2912_CR14","doi-asserted-by":"crossref","unstructured":"Dou, H., Zhang, P., Su, W., Yu, Y., & Li, X. (2022). Metagait: Learning to learn an omni sample adaptive representation for gait recognition. Eur. Conf. Comput. Vis (pp. 357\u2013374)","DOI":"10.1007\/978-3-031-20065-6_21"},{"key":"2912_CR15","doi-asserted-by":"crossref","unstructured":"Dou, H., Zhang, P., Su, W., Yu, Y., Lin, Y., & Li, X. (2023). Gaitgci: Generative counterfactual intervention for gait recognition. IEEE Conf. Comput. Vis. Pattern Recog (pp. 5578\u20135588)","DOI":"10.1109\/CVPR52729.2023.00540"},{"key":"2912_CR16","volume-title":"Gaitmpl: Gait recognition with memory-augmented progressive learning","author":"H Dou","year":"2022","unstructured":"Dou, H., Zhang, P., Zhao, Y., Dong, L., Qin, Z., & Li, X. (2022). Gaitmpl: Gait recognition with memory-augmented progressive learning. Image Process: IEEE Trans."},{"key":"2912_CR17","volume-title":"Clash: Complementary learning with neural architecture search for gait recognition","author":"H Dou","year":"2024","unstructured":"Dou, H., Zhang, P., Zhao, Y., Jin, L., & Li, X. (2024). Clash: Complementary learning with neural architecture search for gait recognition. Image Process: IEEE Trans."},{"key":"2912_CR18","unstructured":"Fan, C., Hou, S., Huang, Y., & Yu, S. (2023). Exploring deep models for practical gait recognition arXiv:2303.03301."},{"key":"2912_CR19","doi-asserted-by":"crossref","unstructured":"Fan, C., Liang, J., Shen, C., Hou, S., Huang, Y., & Yu, S. (2023). Opengait: Revisiting gait recognition towards better practicality. IEEE Conf. Comput. Vis. Pattern Recog (pp. 9707\u20139716)","DOI":"10.1109\/CVPR52729.2023.00936"},{"key":"2912_CR20","doi-asserted-by":"crossref","unstructured":"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. IEEE Conf. Comput. Vis. Pattern Recog (pp. 14225\u201314233)","DOI":"10.1109\/CVPR42600.2020.01423"},{"key":"2912_CR21","doi-asserted-by":"crossref","unstructured":"Feng, Y., Li, Y., & Luo, J. (2016). Learning effective gait features using lstm. Int. Conf. Pattern Recog (pp. 325\u2013330)","DOI":"10.1109\/ICPR.2016.7899654"},{"key":"2912_CR22","doi-asserted-by":"crossref","unstructured":"Fu, Y., Meng, S., Hou, S., Hu, X., & Huang, Y. (2023). Gpgait: Generalized pose-based gait recognition. In: IEEE Int. Conf. Comput. Vis., pp. 19595\u201319604.","DOI":"10.1109\/ICCV51070.2023.01795"},{"key":"2912_CR23","doi-asserted-by":"crossref","unstructured":"Fu, Y., Wei, Y., Zhou, Y., Shi, H., Huang, G., Wang, X., Yao, Z., & Huang, T. (2019). Horizontal pyramid matching for person re-identification. In: AAAI, vol. 33, pp. 8295\u20138302","DOI":"10.1609\/aaai.v33i01.33018295"},{"key":"2912_CR24","doi-asserted-by":"crossref","unstructured":"Guo, H., Wang, H., & Ji, Q. (2022). Uncertainty-guided probabilistic transformer for complex action recognition. IEEE Conf. Comput. Vis. Pattern Recog (pp. 20052\u201320061)","DOI":"10.1109\/CVPR52688.2022.01942"},{"key":"2912_CR25","volume-title":"Gait recognition in the wild: A large-scale benchmark and nas-based baseline","author":"X Guo","year":"2025","unstructured":"Guo, X., Zhu, Z., Yang, T., Lin, B., Huang, J., Deng, J., Huang, G., Zhou, J., & Lu, J. (2025). Gait recognition in the wild: A large-scale benchmark and nas-based baseline. Intell: IEEE Trans. Pattern Anal. Mach."},{"key":"2912_CR26","unstructured":"Hadid, A., Ghahramani, M., Kellokumpu, V., Pietik\u00e4inen, M., Bustard, J., & Nixon, M. (2012). Can gait biometrics be spoofed? In: Int. Conf. Pattern Recog (pp. 3280\u20133283)"},{"issue":"2","key":"2912_CR27","doi-asserted-by":"publisher","first-page":"316","DOI":"10.1109\/TPAMI.2006.38","volume":"28","author":"J Han","year":"2005","unstructured":"Han, J., & Bhanu, B. (2005). Individual recognition using gait energy image. IEEE Trans. Pattern Anal. Mach. Intell., 28(2), 316\u2013322.","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"2912_CR28","doi-asserted-by":"crossref","unstructured":"He, S., Luo, H., Wang, P., Wang, F., Li, H., & Jiang, W. (2021). Transreid: Transformer-based object re-identification. In: IEEE Int. Conf. Comput. Vis., pp. 15013\u201315022","DOI":"10.1109\/ICCV48922.2021.01474"},{"key":"2912_CR29","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep residual learning for image recognition. IEEE Conf. Comput. Vis. Pattern Recog (pp. 770\u2013778)","DOI":"10.1109\/CVPR.2016.90"},{"key":"2912_CR30","doi-asserted-by":"crossref","unstructured":"Hosni, N., & Ben Amor, B. (2020). A geometric convnet on 3d shape manifold for gait recognition. IEEE Conf. Comput. Vis. Pattern Recog. Worksh","DOI":"10.1109\/CVPRW50498.2020.00434"},{"key":"2912_CR31","doi-asserted-by":"crossref","unstructured":"Hou, S., Cao, C., Liu, X., & Huang, Y. (2020). Gait lateral network: Learning discriminative and compact representations for gait recognition. Eur. Conf. Comput. Vis (pp. 382\u2013398)","DOI":"10.1007\/978-3-030-58545-7_22"},{"issue":"3","key":"2912_CR32","doi-asserted-by":"publisher","first-page":"384","DOI":"10.1109\/TBIOM.2021.3074963","volume":"3","author":"S Hou","year":"2021","unstructured":"Hou, S., Liu, X., Cao, C., & Huang, Y. (2021). Set residual network for silhouette-based gait recognition. IEEE trans. biom. behav. identity sci., 3(3), 384\u2013393.","journal-title":"IEEE trans. biom. behav. identity sci."},{"key":"2912_CR33","doi-asserted-by":"crossref","unstructured":"Huang, P., Peng, Y., Hou, S., Cao, C., Liu, X., He, Z., & Huang, Y. (2025). Occluded gait recognition with mixture of experts: An action detection perspective. In: Eur. Conf. Comput. Vis., pp. 380\u2013397.","DOI":"10.1007\/978-3-031-72658-3_22"},{"key":"2912_CR34","doi-asserted-by":"crossref","unstructured":"Huang, Z., Xue, D., Shen, X., Tian, X., Li, H., Huang, J., & Hua, X.-S. (2021). 3d local convolutional neural networks for gait recognition. In: IEEE Int. Conf. Comput. Vis., pp. 14920\u201314929","DOI":"10.1109\/ICCV48922.2021.01465"},{"key":"2912_CR35","doi-asserted-by":"crossref","unstructured":"Huang, X., Zhu, D., Wang, H., Wang, X., Yang, B., He, B., Liu, W., & Feng, B. (2021). Context-sensitive temporal feature learning for gait recognition. In: IEEE Int. Conf. Comput. Vis., pp. 12909\u201312918.","DOI":"10.1109\/ICCV48922.2021.01267"},{"key":"2912_CR36","doi-asserted-by":"crossref","unstructured":"Hu, J., Shen, L., & Sun, G. (2018). Squeeze-and-excitation networks. IEEE Conf. Comput. Vis. Pattern Recog (pp. 7132\u20137141)","DOI":"10.1109\/CVPR.2018.00745"},{"key":"2912_CR37","unstructured":"Ioffe, S., & Szegedy, C. (2015). Batch normalization: Accelerating deep network training by reducing internal covariate shift. International Conference on Machine Learning (pp. 448\u2013456) pmlr."},{"issue":"4","key":"2912_CR38","first-page":"1187","volume":"8","author":"H Iwama","year":"2013","unstructured":"Iwama, H., Muramatsu, D., Makihara, Y., & Yagi, Y. (2013). Gait verification system for criminal investigation. Learn Media Technol., 8(4), 1187\u20131199.","journal-title":"Learn Media Technol."},{"key":"2912_CR39","doi-asserted-by":"crossref","unstructured":"KaewTraKulPong P, Bowden R (2002) An improved adaptive background mixture model for real-time tracking with shadow detection. Image Vis. Comput., 135\u2013144 (2002)","DOI":"10.1007\/978-1-4615-0913-4_11"},{"key":"2912_CR40","doi-asserted-by":"crossref","unstructured":"Kusakunniran, W., Wu, Q., Li, H., & Zhang, J. (2009). Multiple views gait recognition using view transformation model based on optimized gait energy image. IEEE Int. Conf. Comput. Vis (pp. 1058\u20131064)","DOI":"10.1109\/ICCVW.2009.5457587"},{"issue":"5","key":"2912_CR41","doi-asserted-by":"publisher","first-page":"1149","DOI":"10.1111\/j.1556-4029.2008.00807.x","volume":"53","author":"PK Larsen","year":"2008","unstructured":"Larsen, P. K., Simonsen, E. B., & Lynnerup, N. (2008). Gait analysis in forensic medicine. J. Forensic Sci., 53(5), 1149\u20131153.","journal-title":"J. Forensic Sci."},{"key":"2912_CR42","doi-asserted-by":"crossref","unstructured":"Li, W., Hou, S., Zhang, C., Cao, C., Liu, X., Huang, Y., & Zhao, Y. (2023). An in-depth exploration of person re-identification and gait recognition in cloth-changing conditions. In: IEEE Conf. Comput. Vis. Pattern Recog., pp. 13824\u201313833.","DOI":"10.1109\/CVPR52729.2023.01328"},{"key":"2912_CR43","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2019.107069","volume":"98","author":"R Liao","year":"2020","unstructured":"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, Article 107069.","journal-title":"Pattern Recognition"},{"issue":"2","key":"2912_CR44","doi-asserted-by":"publisher","first-page":"1535","DOI":"10.1007\/s10489-022-03543-y","volume":"53","author":"G Li","year":"2023","unstructured":"Li, G., Guo, L., Zhang, R., Qian, J., & Gao, S. (2023). Transgait: Multimodal-based gait recognition with set transformer. Applied Intelligence, 53(2), 1535\u20131547.","journal-title":"Applied Intelligence"},{"key":"2912_CR45","doi-asserted-by":"crossref","unstructured":"Li, X., Makihara, Y., Xu, C., & Yagi, Y. (2021). End-to-end model-based gait recognition using synchronized multi-view pose constraint. IEEE Int. Conf. Comput. Vis (pp. 4106\u20134115)","DOI":"10.1109\/ICCVW54120.2021.00456"},{"key":"2912_CR46","doi-asserted-by":"crossref","unstructured":"Li, X., Makihara, Y., Xu, C., Yagi, Y., Yu, S., & Ren, M. (2020). End-to-end model-based gait recognition. Proc. Asian Conf. Comput. Vis","DOI":"10.1007\/978-3-030-69535-4_1"},{"key":"2912_CR47","doi-asserted-by":"crossref","unstructured":"Lin, B., Zhang, S., & Bao, F. (2020). Gait recognition with multiple-temporal-scale 3d convolutional neural network. ACM Int. Conf. Multimedia (pp. 3054\u20133062)","DOI":"10.1145\/3394171.3413861"},{"key":"2912_CR48","doi-asserted-by":"crossref","unstructured":"Lin, B., Zhang, S., & Yu, X. (2021). Gait recognition via effective global-local feature representation and local temporal aggregation. IEEE Int. Conf. Comput. Vis (pp. 14648\u201314656)","DOI":"10.1109\/ICCV48922.2021.01438"},{"key":"2912_CR49","doi-asserted-by":"crossref","unstructured":"Liu, Z., Lin, Y., Cao, Y., Hu, H., Wei, Y., Zhang, Z., Lin, S., & Guo, B. (2021). Swin transformer: Hierarchical vision transformer using shifted windows. In: IEEE Int. Conf. Comput. Vis., pp. 10012\u201310022","DOI":"10.1109\/ICCV48922.2021.00986"},{"issue":"6","key":"2912_CR50","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/2816795.2818013","volume":"34","author":"M Loper","year":"2015","unstructured":"Loper, M., Mahmood, N., Romero, J., Pons-Moll, G., & Black, M. J. (2015). Smpl: A skinned multi-person linear model. ACM Trans. Graph., 34(6), 1\u201316.","journal-title":"ACM Trans. Graph."},{"key":"2912_CR51","doi-asserted-by":"crossref","unstructured":"Luo, H., Gu, Y., Liao, X., Lai, S., & Jiang, W. (2019). Bag of tricks and a strong baseline for deep person re-identification. In: IEEE Conf. Comput. Vis. Pattern Recog. Worksh., pp. 0\u20130.","DOI":"10.1109\/CVPRW.2019.00190"},{"key":"2912_CR52","doi-asserted-by":"crossref","unstructured":"Ma, K., Fu, Y., Zheng, D., Cao, C., Hu, X., & Huang, Y. (2023). Dynamic aggregated network for gait recognition. In: IEEE Conf. Comput. Vis. Pattern Recog., pp. 22076\u201322085","DOI":"10.1109\/CVPR52729.2023.02114"},{"key":"2912_CR53","unstructured":"Maaten, L., & Hinton, G. (2008). Visualizing data using t-sne. Journal of machine learning research, 9(11)."},{"key":"2912_CR54","doi-asserted-by":"crossref","unstructured":"Ma, K., Fu, Y., Cao, C., Hou, S., Huang, Y., & Zheng, D. (2024). Learning visual prompt for gait recognition. IEEE Conf. Comput. Vis. Pattern Recog (pp. 593\u2013603)","DOI":"10.1109\/CVPR52733.2024.00063"},{"key":"2912_CR55","doi-asserted-by":"crossref","unstructured":"Makihara, Y., Suzuki, A., Muramatsu, D., Li, X., & Yagi, Y. (2017). Joint intensity and spatial metric learning for robust gait recognition. IEEE Conf. Comput. Vis. Pattern Recog (pp. 5705\u20135715)","DOI":"10.1109\/CVPR.2017.718"},{"issue":"2","key":"2912_CR56","doi-asserted-by":"publisher","first-page":"617","DOI":"10.1109\/T-AIEE.1928.5055024","volume":"47","author":"H Nyquist","year":"1928","unstructured":"Nyquist, H. (1928). Certain topics in telegraph transmission theory. Transactions of the American Institute of Electrical Engineers, 47(2), 617\u2013644.","journal-title":"Transactions of the American Institute of Electrical Engineers"},{"key":"2912_CR57","doi-asserted-by":"crossref","unstructured":"Peng, Y., Cao, C., & He, Z. (2023). Occluded gait recognition. International Joint Conference on Neural Networks (pp. 1\u20138)","DOI":"10.1109\/IJCNN54540.2023.10191651"},{"issue":"1","key":"2912_CR58","doi-asserted-by":"publisher","first-page":"10","DOI":"10.1109\/JRPROC.1949.232969","volume":"37","author":"CE Shannon","year":"1949","unstructured":"Shannon, C. E. (1949). Communication in the presence of noise. Proceedings of the IRE, 37(1), 10\u201321.","journal-title":"Proceedings of the IRE"},{"key":"2912_CR59","doi-asserted-by":"crossref","unstructured":"Shen, C., Fan, C., Wu, W., Wang, R., Huang, G. Q., & Yu, S. (2023). Lidargait: Benchmarking 3d gait recognition with point clouds. IEEE Conf. Comput. Vis. Pattern Recog (pp. 1054\u20131063)","DOI":"10.1109\/CVPR52729.2023.00108"},{"key":"2912_CR60","unstructured":"Springenberg, J. T., Dosovitskiy, A., Brox, T., & Riedmiller, M. (2014). Striving for simplicity: The all convolutional net arXiv:1412.6806."},{"key":"2912_CR61","doi-asserted-by":"crossref","unstructured":"Sun, Y., Zheng, L., Yang, Y., Tian, Q., & Wang, S. (2018). Beyond part models: Person retrieval with refined part pooling. Eur. Conf. Comput. Vis (pp. 480\u2013496)","DOI":"10.1007\/978-3-030-01225-0_30"},{"key":"2912_CR62","first-page":"1","volume":"10","author":"N Takemura","year":"2018","unstructured":"Takemura, N., Makihara, Y., Muramatsu, D., Echigo, T., & Yagi, Y. (2018). Multi-view large population gait dataset and its performance evaluation for cross-view gait recognition. IPSJ Trans. Comput. Vis. Appl., 10, 1\u201314.","journal-title":"IPSJ Trans. Comput. Vis. Appl."},{"key":"2912_CR63","doi-asserted-by":"crossref","unstructured":"Tang, Y., Han, K., Guo, J., Xu, C., Li, Y., Xu, C., & Wang, Y. (2022). An image patch is a wave: Phase-aware vision mlp. In: IEEE Conf. Comput. Vis. Pattern Recog., pp. 10935\u201310944.","DOI":"10.1109\/CVPR52688.2022.01066"},{"issue":"10","key":"2912_CR64","doi-asserted-by":"publisher","first-page":"1700","DOI":"10.1109\/TPAMI.2007.1096","volume":"29","author":"D Tao","year":"2007","unstructured":"Tao, D., Li, X., Wu, X., & Maybank, S. J. (2007). General tensor discriminant analysis and gabor features for gait recognition. IEEE Trans. Pattern Anal. Mach. Intell., 29(10), 1700\u20131715.","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"2912_CR65","doi-asserted-by":"crossref","unstructured":"Teepe, T., Gilg, J., Herzog, F., H\u00f6rmann, S., & Rigoll, G. (2022). Towards a deeper understanding of skeleton-based gait recognition. IEEE Conf. Comput. Vis. Pattern Recog. Worksh (pp. 1569\u20131577)","DOI":"10.1109\/CVPRW56347.2022.00163"},{"key":"2912_CR66","doi-asserted-by":"crossref","unstructured":"Teepe, T., Khan, A., Gilg, J., Herzog, F., H\u00f6rmann, S., & Rigoll, G. (2021). Gaitgraph: graph convolutional network for skeleton-based gait recognition. IEEE Int. Conf. Image Process","DOI":"10.1109\/ICIP42928.2021.9506717"},{"key":"2912_CR67","doi-asserted-by":"crossref","unstructured":"Truong, T.-D., Bui, Q.-H., Duong, C. N., Seo, H.-S., Phung, S. L., Li, X., & Luu, K. (2022). Direcformer: A directed attention in transformer approach to robust action recognition. IEEE Conf. Comput. Vis. Pattern Recog (pp. 20030\u201320040)","DOI":"10.1109\/CVPR52688.2022.01940"},{"key":"2912_CR68","unstructured":"Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, \u0141., & Polosukhin, I. (2017). Attention is all you need. Adv. Neural Inform. Process. Syst. 30"},{"key":"2912_CR69","doi-asserted-by":"crossref","unstructured":"Wagg, D. K., & Nixon, M. S. (2004). On automated model-based extraction and analysis of gait. IEEE Int. Conf. Aut. Fac. Ges. Recog, 11\u201316.","DOI":"10.1109\/AFGR.2004.1301502"},{"key":"2912_CR70","doi-asserted-by":"crossref","unstructured":"Wang, L., Liu, B., Liang, F., & Wang, B. (2023). Hierarchical spatio-temporal representation learning for gait recognition. In: IEEE Int. Conf. Comput. Vis., pp. 19639\u201319649","DOI":"10.1109\/ICCV51070.2023.01799"},{"key":"2912_CR71","doi-asserted-by":"crossref","unstructured":"Wang, X., Girshick, R., Gupta, A., & He, K. (2018). Non-local neural networks. IEEE Conf. Comput. Vis. Pattern Recog (pp. 7794\u20137803)","DOI":"10.1109\/CVPR.2018.00813"},{"key":"2912_CR72","doi-asserted-by":"crossref","unstructured":"Wang, M., Guo, X., Lin, B., Yang, T., Zhu, Z., Li, L., Zhang, S., & Yu, X. (2023). Dygait: Exploiting dynamic representations for high-performance gait recognition. IEEE Int. Conf. Comput. Vis (pp. 13424\u201313433)","DOI":"10.1109\/ICCV51070.2023.01235"},{"key":"2912_CR73","doi-asserted-by":"crossref","unstructured":"Wang, J., & Torresani, L. (2022). Deformable video transformer. IEEE Conf. Comput. Vis. Pattern Recog (pp. 14053\u201314062)","DOI":"10.1109\/CVPR52688.2022.01366"},{"key":"2912_CR74","doi-asserted-by":"crossref","unstructured":"Wang, Z., & Wu, Q. (2025). Waveloss: An adaptive dynamic loss for deep gait recognition (Vol. 39, pp. 8259\u20138267). AAAI.","DOI":"10.1609\/aaai.v39i8.32891"},{"issue":"11","key":"2912_CR75","doi-asserted-by":"publisher","first-page":"2164","DOI":"10.1109\/TPAMI.2011.260","volume":"34","author":"C Wang","year":"2011","unstructured":"Wang, C., Zhang, J., Wang, L., Pu, J., & Yuan, X. (2011). Human identification using temporal information preserving gait template. IEEE Trans. Pattern Anal. Mach. Intell., 34(11), 2164\u20132176.","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"2912_CR76","volume-title":"A survey on gait recognition","author":"C Wan","year":"2018","unstructured":"Wan, C., Wang, L., & Phoha, V. V. (2018). A survey on gait recognition. Surv: ACM Comput."},{"key":"2912_CR77","doi-asserted-by":"crossref","unstructured":"Wolf, T., Babaee, M., & Rigoll, G. (2016). Multi-view gait recognition using 3d convolutional neural networks. IEEE Int. Conf. Image Process (pp. 4165\u20134169)","DOI":"10.1109\/ICIP.2016.7533144"},{"issue":"2","key":"2912_CR78","doi-asserted-by":"publisher","first-page":"209","DOI":"10.1109\/TPAMI.2016.2545669","volume":"39","author":"Z Wu","year":"2016","unstructured":"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 Trans. Pattern Anal. Mach. Intell., 39(2), 209\u2013226.","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"2912_CR79","doi-asserted-by":"crossref","unstructured":"Xiao, J., Jing, L., Zhang, L., He, J., She, Q., Zhou, Z., Yuille, A., & Li, Y. (2022). Learning from temporal gradient for semi-supervised action recognition. IEEE Conf. Comput. Vis. Pattern Recog (pp. 3252\u20133262)","DOI":"10.1109\/CVPR52688.2022.00325"},{"key":"2912_CR80","doi-asserted-by":"crossref","unstructured":"Xiong, H., Feng, B., Wang, X., & Liu, W. (2024). Causality-inspired discriminative feature learning in triple domains for gait recognition arXiv:2407.12519.","DOI":"10.1007\/978-3-031-72949-2_15"},{"key":"2912_CR81","doi-asserted-by":"crossref","unstructured":"Yamauchi, K., Bhanu, B., & Saito, H. (2010). 3d human body modeling using range data. Int. Conf. Pattern Recog (pp. 3476\u20133479)","DOI":"10.1109\/ICPR.2010.849"},{"issue":"5","key":"2912_CR82","doi-asserted-by":"publisher","first-page":"1057","DOI":"10.1016\/j.patcog.2003.09.012","volume":"37","author":"C Yam","year":"2004","unstructured":"Yam, C., Nixon, M. S., & Carter, J. N. (2004). Automated person recognition by walking and running via model-based approaches. Pattern Recognition, 37(5), 1057\u20131072.","journal-title":"Pattern Recognition"},{"key":"2912_CR83","doi-asserted-by":"crossref","unstructured":"Yang, S., Wang, J., Hou, S., Liu, X., Cao, C., Wang, L., & Huang, Y. (2025). Bridging gait recognition and large language models sequence modeling. In: IEEE Conf. Comput. Vis. Pattern Recog., pp. 3460\u20133469","DOI":"10.1109\/CVPR52734.2025.00328"},{"key":"2912_CR84","doi-asserted-by":"crossref","unstructured":"Yang, J., Dong, X., Liu, L., Zhang, C., Shen, J., & Yu, D. (2022). Recurring the transformer for video action recognition. IEEE Conf. Comput. Vis. Pattern Recog (pp. 14063\u201314073)","DOI":"10.1109\/CVPR52688.2022.01367"},{"key":"2912_CR85","doi-asserted-by":"crossref","unstructured":"Ye, D., Fan, C., Ma, J., Liu, X., & Yu, S. (2024). Biggait: Learning gait representation you want by large vision models. IEEE Conf. Comput. Vis. Pattern Recog (pp. 200\u2013210)","DOI":"10.1109\/CVPR52733.2024.00027"},{"key":"2912_CR86","doi-asserted-by":"crossref","unstructured":"Yu, S., Tan, D., & Tan, T. (2006). A framework for evaluating the effect of view angle, clothing and carrying condition on gait recognition. In: Int. Conf. Pattern Recog., vol. 4, pp. 441\u2013444","DOI":"10.1109\/ICPR.2006.67"},{"key":"2912_CR87","doi-asserted-by":"crossref","unstructured":"Zhang, K., Luo, W., Ma, L., Liu, W., & Li, H. (2019). Learning joint gait representation via quintuplet loss minimization. IEEE Conf. Comput. Vis. Pattern Recog (pp. 4700\u20134709)","DOI":"10.1109\/CVPR.2019.00483"},{"key":"2912_CR88","doi-asserted-by":"crossref","unstructured":"Zheng, J., Liu, X., Liu, W., He, L., Yan, C., & Mei, T. (2022). Gait recognition in the wild with dense 3d representations and a benchmark. In: IEEE Conf. Comput. Vis. Pattern Recog., pp. 20228\u201320237.","DOI":"10.1109\/CVPR52688.2022.01959"},{"key":"2912_CR89","doi-asserted-by":"crossref","unstructured":"Zhou, B., Khosla, A., Lapedriza, A., Oliva, A., & Torralba, A. (2016). Learning deep features for discriminative localization. IEEE Conf. Comput. Vis. Pattern Recog (pp. 2921\u20132929)","DOI":"10.1109\/CVPR.2016.319"},{"key":"2912_CR90","unstructured":"Zhu, Z., Guo, X., Yang, T., Huang, J., Deng, J., Huang, G., Du, D., Lu, J., & Zhou, J. (2021). Gait recognition in the wild: A benchmark. In: IEEE Int. Conf. Comput. Vis., pp. 14789\u201314799"},{"key":"2912_CR91","doi-asserted-by":"crossref","unstructured":"Zhu, H., Ke, W., Li, D., Liu, J., Tian, L., & Shan, Y. (2022). Dual cross-attention learning for fine-grained visual categorization and object re-identification. In: IEEE Conf. Comput. Vis. Pattern Recog., pp. 4692\u20134702","DOI":"10.1109\/CVPR52688.2022.00465"},{"key":"2912_CR92","doi-asserted-by":"crossref","unstructured":"Zivkovic, Z. (2004). Improved adaptive gaussian mixture model for background subtraction. Int. Conf. Pattern Recog (Vol. 2, pp. 28\u201331)","DOI":"10.1109\/ICPR.2004.1333992"}],"container-title":["International Journal of Computer Vision"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11263-026-02912-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11263-026-02912-1","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11263-026-02912-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,6,19]],"date-time":"2026-06-19T12:32:52Z","timestamp":1781872372000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11263-026-02912-1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,6,19]]},"references-count":92,"journal-issue":{"issue":"7","published-print":{"date-parts":[[2026,7]]}},"alternative-id":["2912"],"URL":"https:\/\/doi.org\/10.1007\/s11263-026-02912-1","relation":{},"ISSN":["0920-5691","1573-1405"],"issn-type":[{"value":"0920-5691","type":"print"},{"value":"1573-1405","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,6,19]]},"assertion":[{"value":"20 January 2026","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"26 May 2026","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"19 June 2026","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors have no relevant financial or non-financial interests to disclose.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflicts of Interest"}}],"article-number":"323"}}