{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,2]],"date-time":"2026-01-02T00:14:23Z","timestamp":1767312863974,"version":"3.48.0"},"publisher-location":"Cham","reference-count":35,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783032073426","type":"print"},{"value":"9783032073433","type":"electronic"}],"license":[{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026]]},"DOI":"10.1007\/978-3-032-07343-3_6","type":"book-chapter","created":{"date-parts":[[2026,1,2]],"date-time":"2026-01-02T00:09:34Z","timestamp":1767312574000},"page":"71-82","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Gait Recognition via\u00a0Pristine Feature Learning"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4688-9186","authenticated-orcid":false,"given":"Anuj","family":"Rathore","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2871-0376","authenticated-orcid":false,"given":"Daksh","family":"Thapar","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3997-2301","authenticated-orcid":false,"given":"Mahesh","family":"Chandran","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2026,1,2]]},"reference":[{"issue":"3","key":"6_CR1","doi-asserted-by":"publisher","first-page":"257","DOI":"10.1109\/34.910878","volume":"23","author":"A Bobick","year":"2001","unstructured":"Bobick, A., Davis, J.: The recognition of human movement using temporal templates. IEEE Trans. Pattern Anal. Mach. Intell. 23(3), 257\u2013267 (2001). https:\/\/doi.org\/10.1109\/34.910878","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"6_CR2","doi-asserted-by":"crossref","unstructured":"Chao, H., He, Y., Zhang, J., Feng, J.: Gaitset: regarding gait as a set for cross-view gait recognition. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol.\u00a033, pp. 8126\u20138133 (2019)","DOI":"10.1609\/aaai.v33i01.33018126"},{"key":"6_CR3","doi-asserted-by":"crossref","unstructured":"Fan, C., Liang, J., Shen, C., Hou, S., Huang, Y., Yu, S.: Opengait: revisiting gait recognition toward better practicality. arXiv preprint arXiv:2211.06597 (2022)","DOI":"10.1109\/CVPR52729.2023.00936"},{"key":"6_CR4","doi-asserted-by":"crossref","unstructured":"Fan, C., et al.: Gaitpart: temporal part-based model for gait recognition. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 14225\u201314233 (2020)","DOI":"10.1109\/CVPR42600.2020.01423"},{"key":"6_CR5","doi-asserted-by":"crossref","unstructured":"Fankhauser, P., Bloesch, M., Rodriguez, D., Kaestner, R., Hutter, M., Siegwart, R.Y.: Kinect v2 for mobile robot navigation: evaluation and modeling. In: 2015 International Conference on Advanced Robotics (ICAR), pp. 388\u2013394 (2015). https:\/\/api.semanticscholar.org\/CorpusID:206722232","DOI":"10.1109\/ICAR.2015.7251485"},{"key":"6_CR6","doi-asserted-by":"publisher","unstructured":"Fankhauser, P., Bloesch, M., Rodriguez, D., Kaestner, R., Hutter, M., Siegwart, R.: Kinect v2 for mobile robot navigation: evaluation and modeling. In: 2015 International Conference on Advanced Robotics (ICAR), pp. 388\u2013394 (2015). https:\/\/doi.org\/10.1109\/ICAR.2015.7251485","DOI":"10.1109\/ICAR.2015.7251485"},{"key":"6_CR7","unstructured":"Goyal, A., Law, H., Liu, B., Newell, A., Deng, J.: Revisiting point cloud shape classification with a simple and effective baseline. In: Meila, M., Zhang, T. (eds.) Proceedings of the 38th International Conference on Machine Learning, ICML 2021, 18\u201324 July 2021, Virtual Event. Proceedings of Machine Learning Research, vol.\u00a0139, pp. 3809\u20133820. PMLR (2021). http:\/\/proceedings.mlr.press\/v139\/goyal21a.html"},{"key":"6_CR8","doi-asserted-by":"crossref","unstructured":"Guo, W., Liang, Y., Pan, Z., Xi, Z., Feng, J., Zhou, J.: Camera-lidar cross-modality gait recognition (2024)","DOI":"10.1007\/978-3-031-72754-2_25"},{"key":"6_CR9","unstructured":"Guo, Y., Shah, A., Liu, J., Gupta, A., Chellappa, R., Peng, C.: Gaitcontour: efficient gait recognition based on a contour-pose representation (2023)"},{"issue":"2","key":"6_CR10","doi-asserted-by":"publisher","first-page":"316","DOI":"10.1109\/TPAMI.2006.38","volume":"28","author":"J Han","year":"2006","unstructured":"Han, J., Bhanu, B.: Individual recognition using gait energy image. IEEE Trans. Pattern Anal. Mach. Intell. 28(2), 316\u2013322 (2006). https:\/\/doi.org\/10.1109\/TPAMI.2006.38","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"6_CR11","doi-asserted-by":"publisher","unstructured":"Hofmann, M., Geiger, J., Bachmann, S., Schuller, B., Rigoll, G.: The tum gait from audio, image and depth (gaid) database: multimodal recognition of subjects and traits. J. Vis. Commun. Image Represent. 25(1), 195\u2013206 (2014). https:\/\/doi.org\/10.1016\/j.jvcir.2013.02.006. https:\/\/api.semanticscholar.org\/CorpusID:17445139","DOI":"10.1016\/j.jvcir.2013.02.006"},{"key":"6_CR12","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"382","DOI":"10.1007\/978-3-030-58545-7_22","volume-title":"Computer Vision \u2013 ECCV 2020","author":"S Hou","year":"2020","unstructured":"Hou, S., Cao, C., Liu, X., Huang, Y.: Gait lateral network: learning discriminative and compact representations for gait recognition. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12354, pp. 382\u2013398. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-58545-7_22"},{"issue":"2","key":"6_CR13","doi-asserted-by":"publisher","first-page":"85","DOI":"10.1080\/02702710050084428","volume":"21","author":"RM Joshi","year":"2000","unstructured":"Joshi, R.M., Aaron, P.: The component model of reading: simple view of reading made a little more complex. Read. Psychol. 21(2), 85\u201397 (2000)","journal-title":"Read. Psychol."},{"key":"6_CR14","doi-asserted-by":"publisher","unstructured":"Li, X., Makihara, Y., Xu, C., Yagi, Y.: End-to-end model-based gait recognition using synchronized multi-view pose constraint. In: 2021 IEEE\/CVF International Conference on Computer Vision Workshops (ICCVW), pp. 4089\u20134098 (2021). https:\/\/doi.org\/10.1109\/ICCVW54120.2021.00456","DOI":"10.1109\/ICCVW54120.2021.00456"},{"key":"6_CR15","doi-asserted-by":"publisher","unstructured":"Liao, R., Yu, S., An, W., Huang, Y.: A model-based gait recognition method with body pose and human prior knowledge. Pattern Recogn. 98(C) (2020). https:\/\/doi.org\/10.1016\/j.patcog.2019.107069","DOI":"10.1016\/j.patcog.2019.107069"},{"key":"6_CR16","unstructured":"Lin, B., Liu, C., Wang, M., Li, L., Zhang, S., Tan, R.T., Yu, X.: Uncertainty-aware gait recognition via learning from dirichlet distribution-based evidence. arXiv e-prints pp. arXiv\u20132211 (2022)"},{"key":"6_CR17","doi-asserted-by":"crossref","unstructured":"Lin, B., Liu, Y., Zhang, S.: GaitMask: mask-based model for gait recognition. In: Proceedings of the British Machine Vision Conference (BMVC). Virtual Conference (2021). https:\/\/www.bmvc2021-virtualconference.com\/assets\/papers\/0471.pdf","DOI":"10.5244\/C.35.134"},{"key":"6_CR18","unstructured":"Lin, B., Zhang, S., Wang, M., Li, L., Yu, X.: Gaitgl: learning discriminative global-local feature representations for gait recognition. arXiv preprint arXiv:2208.01380 (2022)"},{"issue":"23","key":"6_CR19","doi-asserted-by":"publisher","first-page":"4785","DOI":"10.3390\/electronics12234785","volume":"12","author":"C Ma","year":"2023","unstructured":"Ma, C., Liu, Z.: A novel spatial\u2013temporal network for gait recognition using millimeter-wave radar point cloud videos. Electronics 12(23), 4785 (2023). https:\/\/doi.org\/10.3390\/electronics12234785","journal-title":"Electronics"},{"key":"6_CR20","doi-asserted-by":"crossref","unstructured":"Ma, K., Fu, Y., Zheng, D., Cao, C., Hu, X., Huang, Y.: Dynamic aggregated network for gait recognition. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 22076\u201322085 (2023)","DOI":"10.1109\/CVPR52729.2023.02114"},{"issue":"6","key":"6_CR21","doi-asserted-by":"publisher","first-page":"6832","DOI":"10.1109\/TPAMI.2021.3118077","volume":"45","author":"P Nagar","year":"2023","unstructured":"Nagar, P., Rathore, A., Jawahar, C.V., Arora, C.: Generating personalized summaries of day long egocentric videos. IEEE Trans. Pattern Anal. Mach. Intell. 45(6), 6832\u20136845 (2023). https:\/\/doi.org\/10.1109\/TPAMI.2021.3118077","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"6_CR22","unstructured":"Qi, C.R., Su, H., Mo, K., Guibas, L.J.: Pointnet: deep learning on point sets for 3d classification and segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2017)"},{"key":"6_CR23","unstructured":"Qi, C.R., Yi, L., Su, H., Guibas, L.J.: Pointnet++: deep hierarchical feature learning on point sets in a metric space. In: Proceedings of the Advances in Neural Information Processing Systems (NeurIPS), vol.\u00a030 (2017)"},{"key":"6_CR24","doi-asserted-by":"publisher","unstructured":"Rathore, A., Nagar, P., Arora, C., Jawahar, C.: Generating 1 minute summaries of day long egocentric videos. In: Proceedings of the 27th ACM International Conference on Multimedia, MM \u201919, pp. 2305\u20132313. Association for Computing Machinery, New York (2019). https:\/\/doi.org\/10.1145\/3343031.3350880","DOI":"10.1145\/3343031.3350880"},{"key":"6_CR25","unstructured":"Ren, H., Chen, J., Velipasalar, S.: Gaitpoint+: a gait recognition network incorporating point cloud analysis and recycling (2024)"},{"key":"6_CR26","doi-asserted-by":"publisher","unstructured":"Shen, C., Chao, F., Wu, W., Wang, R., Huang, G.Q., Yu, S.: Lidargait: benchmarking 3d gait recognition with point clouds. In: IEEE\/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2023, Vancouver, BC, Canada, 17\u201324 June 2023, pp. 1054\u20131063. IEEE (2023). https:\/\/doi.org\/10.1109\/CVPR52729.2023.00108","DOI":"10.1109\/CVPR52729.2023.00108"},{"key":"6_CR27","doi-asserted-by":"crossref","unstructured":"Sokolova, A., Konushin, A.: Pose-based deep gait recognition (2018)","DOI":"10.1049\/iet-bmt.2018.5046"},{"key":"6_CR28","doi-asserted-by":"publisher","unstructured":"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, 4 (2018) https:\/\/doi.org\/10.1186\/S41074-018-0039-6","DOI":"10.1186\/S41074-018-0039-6"},{"key":"6_CR29","doi-asserted-by":"publisher","unstructured":"Teepe, T., Khan, A., Gilg, J., Herzog, F., Hormann, S., Rigoll, G.: Gaitgraph: graph convolutional network for skeleton-based gait recognition. In: 2021 IEEE International Conference on Image Processing (ICIP). IEEE (2021). https:\/\/doi.org\/10.1109\/icip42928.2021.9506717","DOI":"10.1109\/icip42928.2021.9506717"},{"key":"6_CR30","doi-asserted-by":"publisher","unstructured":"Tran, D., Bourdev, L., Fergus, R., Torresani, L., Paluri, M.: Learning spatiotemporal features with 3d convolutional networks. In: 2015 IEEE International Conference on Computer Vision (ICCV), pp. 4489\u20134497 (2015). https:\/\/doi.org\/10.1109\/ICCV.2015.510","DOI":"10.1109\/ICCV.2015.510"},{"issue":"12","key":"6_CR31","doi-asserted-by":"publisher","first-page":"1505","DOI":"10.1109\/TPAMI.2003.1251144","volume":"25","author":"L Wang","year":"2003","unstructured":"Wang, L., Tan, T., Ning, H., Hu, W.: Silhouette analysis-based gait recognition for human identification. IEEE Trans. Pattern Anal. Mach. Intell. 25(12), 1505\u20131518 (2003). https:\/\/doi.org\/10.1109\/TPAMI.2003.1251144","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"6_CR32","doi-asserted-by":"publisher","unstructured":"Wang, R., et al.: Gait recognition with multi-level skeleton-guided refinement. IEEE Trans. Multimedia 1\u201312 (2023). https:\/\/doi.org\/10.1109\/TMM.2023.3323887","DOI":"10.1109\/TMM.2023.3323887"},{"key":"6_CR33","doi-asserted-by":"publisher","unstructured":"Yu, S., Tan, D., Tan, T.: 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), Hong Kong, China, 20\u201324 August 2006, pp. 441\u2013444. IEEE Computer Society (2006). https:\/\/doi.org\/10.1109\/ICPR.2006.67","DOI":"10.1109\/ICPR.2006.67"},{"key":"6_CR34","doi-asserted-by":"crossref","unstructured":"Zhao, H., Jiang, L., Jia, J., Torr, P.H., Koltun, V.: Point transformer. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 16259\u201316268 (2021)","DOI":"10.1109\/ICCV48922.2021.01595"},{"key":"6_CR35","doi-asserted-by":"publisher","unstructured":"Zhu, X., Guo, X., Yang, T.: Gait recognition in the wild: a benchmark. In: 2021 IEEE\/CVF International Conference on Computer Vision (ICCV), pp. 14769\u201314779 (2021). https:\/\/doi.org\/10.1109\/ICCV48922.2021.01452","DOI":"10.1109\/ICCV48922.2021.01452"}],"container-title":["Lecture Notes in Computer Science","Advanced Concepts for Intelligent Vision Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-032-07343-3_6","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,1,2]],"date-time":"2026-01-02T00:09:37Z","timestamp":1767312577000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-032-07343-3_6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026]]},"ISBN":["9783032073426","9783032073433"],"references-count":35,"URL":"https:\/\/doi.org\/10.1007\/978-3-032-07343-3_6","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026]]},"assertion":[{"value":"2 January 2026","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ACIVS","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Advanced Concepts for Intelligent Vision Systems","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Tokyo","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Japan","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2025","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"28 July 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"30 July 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"22","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"acivs2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.acivs2025.com","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}