{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,8]],"date-time":"2026-06-08T15:58:16Z","timestamp":1780934296103,"version":"3.54.1"},"reference-count":38,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,12,1]],"date-time":"2026-12-01T00:00:00Z","timestamp":1796083200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,12,1]],"date-time":"2026-12-01T00:00:00Z","timestamp":1796083200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,5,16]],"date-time":"2026-05-16T00:00:00Z","timestamp":1778889600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Pattern Recognition"],"published-print":{"date-parts":[[2026,12]]},"DOI":"10.1016\/j.patcog.2026.113869","type":"journal-article","created":{"date-parts":[[2026,5,13]],"date-time":"2026-05-13T22:42:49Z","timestamp":1778712169000},"page":"113869","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"PA","title":["Individual identification using a novel 3D gait model from human joints and Joint-Features Set Abstraction"],"prefix":"10.1016","volume":"180","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4740-1464","authenticated-orcid":false,"given":"Jegoon","family":"Ryu","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tatsuya","family":"Kawakami","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sei-ichiro","family":"Kamata","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"issue":"5","key":"10.1016\/j.patcog.2026.113869_b1","doi-asserted-by":"crossref","first-page":"353","DOI":"10.3758\/BF03337021","article-title":"Recognizing friends by their walk: gait perception without familiarity cues","volume":"9","author":"Cutting","year":"1977","journal-title":"Bull. Psychon. Soc."},{"issue":"4","key":"10.1016\/j.patcog.2026.113869_b2","doi-asserted-by":"crossref","first-page":"23","DOI":"10.1016\/S1363-4127(02)00404-1","article-title":"New advances in automatic gait recognition","volume":"7","author":"Nixon","year":"2002","journal-title":"Inf. Secur. Tech. Rep."},{"issue":"4","key":"10.1016\/j.patcog.2026.113869_b3","doi-asserted-by":"crossref","first-page":"1062","DOI":"10.1016\/j.gaitpost.2014.01.008","article-title":"Accuracy of the microsoft kinect sensor for measuring movement in people with parkinson\u015b disease","volume":"39","author":"Galna","year":"2014","journal-title":"Gait & Posture"},{"issue":"2","key":"10.1016\/j.patcog.2026.113869_b4","doi-asserted-by":"crossref","first-page":"162","DOI":"10.1109\/TPAMI.2005.39","article-title":"The humanid gait challenge problem: data sets, performance, and analysis","volume":"27","author":"Sarkar","year":"2005","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"issue":"6","key":"10.1016\/j.patcog.2026.113869_b5","doi-asserted-by":"crossref","first-page":"78","DOI":"10.1109\/MSP.2005.1550191","article-title":"Gait recognition: a challenging signal processing technology for biometric identification","volume":"22","author":"Boulgouris","year":"2005","journal-title":"IEEE Signal Process. Mag."},{"key":"10.1016\/j.patcog.2026.113869_b6","first-page":"138","article-title":"Gender classification in human gait using support vector machine","volume":"vol. 3708","author":"Yoo","year":"2005"},{"issue":"21","key":"10.1016\/j.patcog.2026.113869_b7","doi-asserted-by":"crossref","first-page":"8961","DOI":"10.3390\/s23218961","article-title":"Gender recognition based on gradual and ensemble learning from multi-view gait energy images and poses","volume":"23","author":"Leung","year":"2023","journal-title":"Sensors"},{"issue":"2","key":"10.1016\/j.patcog.2026.113869_b8","doi-asserted-by":"crossref","first-page":"316","DOI":"10.1109\/TPAMI.2006.38","article-title":"Individual recognition using gait energy image","volume":"28","author":"Han","year":"2006","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"10.1016\/j.patcog.2026.113869_b9","doi-asserted-by":"crossref","unstructured":"K. Bashir, T. Xiang, S. Gong, Gait recognition using gait entropy image, in: Int. Conf. Imaging Crime Detect. Prev., 2009, pp. 1\u20136.","DOI":"10.1049\/ic.2009.0230"},{"key":"10.1016\/j.patcog.2026.113869_b10","doi-asserted-by":"crossref","unstructured":"J. Ni, L. Liang, A new method based on kfda and svm for gait identification, in: Int. Workshop Intell. Syst. Appl., 2009, pp. 600\u2013603.","DOI":"10.1109\/IWISA.2009.5072648"},{"issue":"2","key":"10.1016\/j.patcog.2026.113869_b11","doi-asserted-by":"crossref","first-page":"209","DOI":"10.1109\/TPAMI.2016.2545669","article-title":"A comprehensive study on cross-view gait based human identification with deep cnns","volume":"39","author":"Wu","year":"2017","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"issue":"1","key":"10.1016\/j.patcog.2026.113869_b12","doi-asserted-by":"crossref","DOI":"10.1142\/S0129065719500278","article-title":"Human gait recognition based on frame-by-frame gait energy images and convolutional long short-term memory","volume":"30","author":"Wang","year":"2019","journal-title":"Int. J. Neural Syst."},{"issue":"5","key":"10.1016\/j.patcog.2026.113869_b13","doi-asserted-by":"crossref","first-page":"1057","DOI":"10.1016\/j.patcog.2003.09.012","article-title":"Automated person recognition by walking and running via model-based approaches","volume":"37","author":"Yam","year":"2004","journal-title":"Pattern Recognit."},{"issue":"2","key":"10.1016\/j.patcog.2026.113869_b14","doi-asserted-by":"crossref","first-page":"234","DOI":"10.1109\/TBIOM.2022.3174559","article-title":"Multi-view large population gait database with human meshes and its performance evaluation","volume":"4","author":"Li","year":"2022","journal-title":"IEEE Trans. Biom. Behav. Ident. Sci."},{"key":"10.1016\/j.patcog.2026.113869_b15","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1155\/2015\/763908","article-title":"Gait recognition using gei and afdei","volume":"2015","author":"Luo","year":"2015","journal-title":"Int. J. Opt."},{"key":"10.1016\/j.patcog.2026.113869_b16","doi-asserted-by":"crossref","first-page":"103","DOI":"10.1016\/j.cviu.2017.10.004","article-title":"Improved gait recognition based on specialized deep convolutional neural networks","volume":"164","author":"Alotaibi","year":"2017","journal-title":"Comput. Vis. Image Underst."},{"issue":"6","key":"10.1016\/j.patcog.2026.113869_b17","doi-asserted-by":"crossref","first-page":"4535","DOI":"10.1109\/TPAMI.2025.3546482","article-title":"Gait recognition in the wild: A large-scale benchmark and nas-based baseline","volume":"47","author":"Guo","year":"2025","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"10.1016\/j.patcog.2026.113869_b18","doi-asserted-by":"crossref","DOI":"10.1016\/j.patcog.2026.113147","article-title":"GaitMDF: Gait recognition via motion deformation field","volume":"175","author":"Huo","year":"2026","journal-title":"Pattern Recognit."},{"issue":"2","key":"10.1016\/j.patcog.2026.113869_b19","doi-asserted-by":"crossref","first-page":"327","DOI":"10.1016\/j.patrec.2015.06.020","article-title":"A framework for gait-based recognition using kinect","volume":"68","author":"Kastaniotis","year":"2015","journal-title":"Pattern Recognit Lett"},{"key":"10.1016\/j.patcog.2026.113869_b20","doi-asserted-by":"crossref","first-page":"245","DOI":"10.1016\/j.patrec.2016.10.012","article-title":"Gait based recognition via fusing information from euclidean and Riemannian manifolds","volume":"84","author":"Kastaniotis","year":"2016","journal-title":"Pattern Recognit. Lett."},{"key":"10.1016\/j.patcog.2026.113869_b21","doi-asserted-by":"crossref","unstructured":"G. Guan, T. Yang, W. Liu, Gait recognition with skeleton information by using ensemble learning, in: Int. Congr. Image Signal Process. Biomed. Eng. Inform., 2017, pp. 1\u20137.","DOI":"10.1109\/CISP-BMEI.2017.8302005"},{"issue":"1","key":"10.1016\/j.patcog.2026.113869_b22","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1109\/TIFS.2017.2738611","article-title":"Human identification from freestyle walks using posture-based gait feature","volume":"13","author":"Khamsemanan","year":"2017","journal-title":"IEEE Trans. Inf. Forensics Secur."},{"key":"10.1016\/j.patcog.2026.113869_b23","doi-asserted-by":"crossref","first-page":"192","DOI":"10.1016\/j.neucom.2020.02.048","article-title":"Learning 3d spatiotemporal gait feature by convolutional network for person identification","volume":"397","author":"Huynh-The","year":"2020","journal-title":"Neurocomputing"},{"key":"10.1016\/j.patcog.2026.113869_b24","doi-asserted-by":"crossref","DOI":"10.1016\/j.patcog.2019.107069","article-title":"A model-based gait recognition method with body pose and human prior knowledge","volume":"98","author":"Liao","year":"2020","journal-title":"Pattern Recognition"},{"key":"10.1016\/j.patcog.2026.113869_b25","doi-asserted-by":"crossref","unstructured":"T. Teepe, A. Khan, J. Gilg, F. Herzog, S. Hormann, G. Rigoll, Gaitgraph: graph convolutional network for skeleton-based gait recognition, in: IEEE Int. Conf. Image Process., 2021, pp. 2314\u20132318.","DOI":"10.1109\/ICIP42928.2021.9506717"},{"key":"10.1016\/j.patcog.2026.113869_b26","doi-asserted-by":"crossref","unstructured":"C. Fan, J. Ma, D. Jin, C. Shen, S. Yu, SkeletonGait: Gait Recognition Using Skeleton Maps, in: Proc. IEEE\/CVF Int. Conf. Comput. Vis., 2024, pp. 1662\u20131669.","DOI":"10.1609\/aaai.v38i2.27933"},{"issue":"10","key":"10.1016\/j.patcog.2026.113869_b27","doi-asserted-by":"crossref","first-page":"8397","DOI":"10.1109\/TPAMI.2025.3576283","article-title":"OpenGait: A comprehensive benchmark study for gait recognition toward better practicality","volume":"47","author":"Fan","year":"2025","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"10.1016\/j.patcog.2026.113869_b28","doi-asserted-by":"crossref","unstructured":"S. Yu, D. Tan, T. Tan, A framework for evaluating the effect of view angle, clothing and carrying condition on gait recognition, in: Proc. Int. Conf. Pattern Recognit., 2006, pp. 441\u2013444.","DOI":"10.1109\/ICPR.2006.67"},{"key":"10.1016\/j.patcog.2026.113869_b29","doi-asserted-by":"crossref","unstructured":"M. Kocabas, N. Athanasiou, M. Black, Vibe: video inference for human body pose and shape estimation, in: Proc. IEEE\/CVF Conf. Comput. Vis. Pattern Recognit., 2020, pp. 5252\u20135262.","DOI":"10.1109\/CVPR42600.2020.00530"},{"key":"10.1016\/j.patcog.2026.113869_b30","doi-asserted-by":"crossref","unstructured":"J. Zheng, X. Liu, W. Liu, L. He, C. Yan, T. Mei, Gait Recognition in the Wild with Dense 3D Representations and A Benchmark, in: Proc. IEEE\/CVF Conf. Comput. Vis. Pattern Recognit., 2022, pp. 20228\u201320237.","DOI":"10.1109\/CVPR52688.2022.01959"},{"key":"10.1016\/j.patcog.2026.113869_b31","first-page":"5105","article-title":"Pointnet++: deep hierarchical feature learning on point sets in a metric space","volume":"1","author":"Qi","year":"2017"},{"issue":"5","key":"10.1016\/j.patcog.2026.113869_b32","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3326362","article-title":"Dynamic graph CNN for learning on point clouds","volume":"38","author":"Wang","year":"2019","journal-title":"ACM Trans. Graph."},{"issue":"5","key":"10.1016\/j.patcog.2026.113869_b33","doi-asserted-by":"crossref","first-page":"1511","DOI":"10.1109\/TIFS.2012.2204253","article-title":"The ou-isir gait database comprising the large population dataset and performance evaluation of gait recognition","volume":"7","author":"Iwama","year":"2012","journal-title":"IEEE Trans. Inf. Forensics Secur."},{"issue":"4","key":"10.1016\/j.patcog.2026.113869_b34","doi-asserted-by":"crossref","first-page":"421","DOI":"10.1109\/TBIOM.2020.3008862","article-title":"Performance evaluation of model-based gait on multi-view very large population database with pose sequences","volume":"2","author":"An","year":"2020","journal-title":"IEEE Trans. Biom. Behav. Ident. Sci."},{"key":"10.1016\/j.patcog.2026.113869_b35","unstructured":"Z. Zhu, X. Guo, T. Yang, J. Huang, J. Deng, G. Huang, D. Du, J. Lu, J. Zhou, Gait Recognition in the Wild: A Benchmark, in: Proc. IEEE\/CVF Int. Conf. Comput. Vis., 2021, pp. 14789\u201314799."},{"key":"10.1016\/j.patcog.2026.113869_b36","doi-asserted-by":"crossref","first-page":"7273","DOI":"10.1007\/s11042-023-15483-x","article-title":"Learning rich features for gait recognition by integrating skeletons and silhouettes","volume":"83","author":"Peng","year":"2024","journal-title":"Multimedia Tools Appl."},{"key":"10.1016\/j.patcog.2026.113869_b37","doi-asserted-by":"crossref","unstructured":"H. Choi, G. Moon, J. Chang, K. Lee, Beyond Static Features for Temporally Consistent 3D Human Pose and Shape from a Video, in: Proc. IEEE\/CVF Conf. Comput. Vis. Pattern Recognit., 2021.","DOI":"10.1109\/CVPR46437.2021.00200"},{"key":"10.1016\/j.patcog.2026.113869_b38","doi-asserted-by":"crossref","unstructured":"Z. Li, J. Liu, Z. Zhang, S. Xu, Y. Yan, CLIFF: Carrying Location Information in Full Frames into Human Pose and Shape Estimation, in: Eur. Conf. Comput. Vis., 2022.","DOI":"10.1007\/978-3-031-20065-6_34"}],"container-title":["Pattern Recognition"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0031320326008344?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0031320326008344?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,6,8]],"date-time":"2026-06-08T15:03:17Z","timestamp":1780930997000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0031320326008344"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,12]]},"references-count":38,"alternative-id":["S0031320326008344"],"URL":"https:\/\/doi.org\/10.1016\/j.patcog.2026.113869","relation":{},"ISSN":["0031-3203"],"issn-type":[{"value":"0031-3203","type":"print"}],"subject":[],"published":{"date-parts":[[2026,12]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Individual identification using a novel 3D gait model from human joints and Joint-Features Set Abstraction","name":"articletitle","label":"Article Title"},{"value":"Pattern Recognition","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.patcog.2026.113869","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 The Authors. Published by Elsevier Ltd.","name":"copyright","label":"Copyright"}],"article-number":"113869"}}