{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,30]],"date-time":"2026-06-30T16:13:40Z","timestamp":1782836020001,"version":"3.54.5"},"reference-count":64,"publisher":"Springer Science and Business Media LLC","issue":"2","license":[{"start":{"date-parts":[[2025,4,30]],"date-time":"2025-04-30T00:00:00Z","timestamp":1745971200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2025,4,30]],"date-time":"2025-04-30T00:00:00Z","timestamp":1745971200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/100019997","name":"Gumushane University","doi-asserted-by":"crossref","id":[{"id":"10.13039\/100019997","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Pattern Anal Applic"],"published-print":{"date-parts":[[2025,6]]},"abstract":"<jats:title>Abstract<\/jats:title>\n          <jats:p>Gait, a behavior-based biometric feature, has gained increasing popularity in human identification, particularly in surveillance systems, due to its ability to function without physical contact or explicit consent. Traditional silhouette-based methods have demonstrated that different body parts exhibit distinct movement patterns during walking, thereby enhancing recognition accuracy. In this study, we propose an improved part-based gait recognition approach by leveraging ensemble learning on local body regions. The Gait Energy Image (GEI) is segmented into five horizontal parts, and ensemble learning is applied to the convolutional neural network (CNN) responsible for their processing. A separate MetaModel is trained for each body part to integrate the part-based features obtained from ensemble learning and synthesize the most discriminative ones. Additionally, a part-removal process is introduced to mitigate the effects of appearance-based variations by analyzing absolute differences between images with and without variations. The aggregated most distinctive features contribute to robust recognition. We evaluate our proposed approach on the CASIA-B, CASIA-C, and Outdoor-Gait datasets, and experimental results indicate that ensemble learning significantly enhances part-based gait recognition performance under various appearance variations, outperforming several state-of-the-art methods. The datasets and source code are available at <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" xlink:href=\"https:\/\/github.com\/busrakckugurlu\/Enhancing-Part-based-Gait-Recognition-via-Ensemble-Learning-and-Feature-Fusion\/tree\/main\" ext-link-type=\"uri\">https:\/\/github.com\/busrakckugurlu\/Enhancing-Part-based-Gait-Recognition-via-Ensemble-Learning-and-Feature-Fusion\/tree\/main<\/jats:ext-link>.<\/jats:p>","DOI":"10.1007\/s10044-025-01478-x","type":"journal-article","created":{"date-parts":[[2025,4,30]],"date-time":"2025-04-30T06:01:46Z","timestamp":1745992906000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Enhancing part-based gait recognition via ensemble learning and feature fusion"],"prefix":"10.1007","volume":"28","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6034-6850","authenticated-orcid":false,"given":"B\u00fc\u015franur","family":"Yaprak","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7212-5457","authenticated-orcid":false,"given":"Ey\u00fcp","family":"Gedikli","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,4,30]]},"reference":[{"issue":"2","key":"1478_CR1","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. https:\/\/doi.org\/10.1109\/TPAMI.2006.38","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"issue":"11","key":"1478_CR2","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. https:\/\/doi.org\/10.1109\/TPAMI.2011.260","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"1478_CR3","doi-asserted-by":"crossref","unstructured":"Bashir K, Xiang T, Gong S (2009) Gait recognition using gait entropy image. In: Proceedings of the International Conference on Crime Detection and Prevention (pp.1\u20136)","DOI":"10.1049\/ic.2009.0230"},{"issue":"1","key":"1478_CR4","doi-asserted-by":"publisher","first-page":"1102","DOI":"10.1109\/TIFS.2018.2844819","volume":"14","author":"Y He","year":"2019","unstructured":"He Y, Zhang J, Shan H, Wang L (2019) Multi-task GANs for viewspecific feature learning in gait recognition. IEEE Trans Inf Forensics Secur 14(1):1102\u20131113. https:\/\/doi.org\/10.1109\/TIFS.2018.2844819","journal-title":"IEEE Trans Inf Forensics Secur"},{"key":"1478_CR5","doi-asserted-by":"crossref","unstructured":"Lin B, Zhang S, Yu X (2021) Gait recognition via effective global-local feature representation and local temporal aggregation. In: Proceedings of the IEEE\/CVF international conference on computer vision (pp. 14648\u201314656)","DOI":"10.1109\/ICCV48922.2021.01438"},{"key":"1478_CR6","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. In: AAAI Conference on Artificial Intelligence (pp. 8126\u20138133)","DOI":"10.1609\/aaai.v33i01.33018126"},{"key":"1478_CR7","doi-asserted-by":"publisher","first-page":"1001","DOI":"10.1109\/TIP.2019.2926208","volume":"29","author":"Y Zhang","year":"2019","unstructured":"Zhang Y, Huang Y, Yu S, Wang L (2019) Cross-view gait recognition by discriminative feature learning. IEEE Trans Image Process 29:1001\u20131015. https:\/\/doi.org\/10.1109\/TIP.2019.2926208","journal-title":"IEEE Trans Image Process"},{"issue":"2","key":"1478_CR8","doi-asserted-by":"publisher","first-page":"209","DOI":"10.1109\/TPAMI.2016.2545669","volume":"39","author":"Z Wu","year":"2017","unstructured":"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\u2013226. https:\/\/doi.org\/10.1109\/TPAMI.2016.2545669","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"1478_CR9","doi-asserted-by":"publisher","first-page":"5452","DOI":"10.1109\/TIFS.2021.3132579","volume":"16","author":"MJ Mar\u00edn-Jim\u00e9nez","year":"2021","unstructured":"Mar\u00edn-Jim\u00e9nez MJ, Castro FM, Delgado-Esca\u00f1o R, Kalogeiton V, Guil N (2021) UGaitNet: multimodal gait recognition with missing input modalities. IEEE Trans Inf Forensics Secur 16:5452\u20135462. https:\/\/doi.org\/10.1109\/TIFS.2021.3132579","journal-title":"IEEE Trans Inf Forensics Secur"},{"key":"1478_CR10","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. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition (pp. 14225\u201314233)","DOI":"10.1109\/CVPR42600.2020.01423"},{"key":"1478_CR11","unstructured":"Nixon MS, Carter JN, Nash JM, Huang PS, Cunado D, Stevenage SV (1999) Auto-matic gait recognition. In: Motion Analysis and Tracking (p. 3\/1\u20133\/6)"},{"key":"1478_CR12","doi-asserted-by":"crossref","unstructured":"Feng Y, Li Y, Luo J (2016) Learning effective gait features using LSTM. In: the 23rd International Conference on Pattern Recognition (pp. 325\u2013330)","DOI":"10.1109\/ICPR.2016.7899654"},{"key":"1478_CR13","doi-asserted-by":"publisher","first-page":"107069","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 Recognit 98:107069. https:\/\/doi.org\/10.1016\/j.patcog.2019.107069","journal-title":"Pattern Recognit"},{"key":"1478_CR14","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. In: IEEE international conference on image processing (ICIP) (pp. 2314\u20132318)","DOI":"10.1109\/ICIP42928.2021.9506717"},{"key":"1478_CR15","doi-asserted-by":"publisher","first-page":"3046","DOI":"10.1109\/TMM.2022.3154609","volume":"25","author":"N Li","year":"2022","unstructured":"Li N, Zhao X (2022) A strong and robust skeleton-based gait recognition method with gait periodicity priors. IEEE Trans Multimed 25:3046\u20133058. https:\/\/doi.org\/10.1109\/TMM.2022.3154609","journal-title":"IEEE Trans Multimedia"},{"issue":"4","key":"1478_CR16","doi-asserted-by":"publisher","first-page":"973","DOI":"10.1016\/j.patcog.2010.10.011","volume":"44","author":"TH Lam","year":"2011","unstructured":"Lam TH, Cheung KH, Liu JN (2011) Gait flow image: a silhouette-based gait representation for human identification. Pattern Recognit 44(4):973\u2013987. https:\/\/doi.org\/10.1016\/j.patcog.2010.10.011","journal-title":"Pattern Recognit"},{"issue":"9","key":"1478_CR17","doi-asserted-by":"publisher","first-page":"2708","DOI":"10.1109\/TCSVT.2017.2760835","volume":"29","author":"N Takemura","year":"2017","unstructured":"Takemura N, Makihara Y, Muramatsu D, Echigo T, Yagi Y (2017) On input\/output architectures for convolutional neural network-based cross-view gait recognition. IEEE Trans Circuits Syst Video Technol 29(9):2708\u20132719. https:\/\/doi.org\/10.1109\/TCSVT.2017.2760835","journal-title":"IEEE Trans Circuits Syst Video Technol"},{"key":"1478_CR18","doi-asserted-by":"publisher","first-page":"3653","DOI":"10.1007\/s11227-020-03409-5","volume":"77","author":"O Elharrouss","year":"2021","unstructured":"Elharrouss O, Almaadeed N, Al-Maadeed S, Bouridane A (2021) Gait recognition for person re-identification. J Supercomput 77:3653\u20133672. https:\/\/doi.org\/10.1007\/s11227-020-03409-5","journal-title":"J Supercomput"},{"key":"1478_CR19","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, Springer International Publishing European conference on computer vision (pp. 382\u2013398)","DOI":"10.1007\/978-3-030-58545-7_22"},{"key":"1478_CR20","doi-asserted-by":"crossref","unstructured":"Wolf T, Babaee M, Rigoll G (2016) Multi-view gait recognition using 3D convolutional neural networks, In 2016 IEEE international conference on image processing (ICIP) (pp. 4165\u20134169)","DOI":"10.1109\/ICIP.2016.7533144"},{"issue":"1","key":"1478_CR21","doi-asserted-by":"publisher","first-page":"1565","DOI":"10.1007\/s11042-020-09777-7","volume":"80","author":"X Wang","year":"2021","unstructured":"Wang X, Yan K (2021) Gait classification through CNN-based ensemble learning. Multimed Tools Appl 80(1):1565\u20131581. https:\/\/doi.org\/10.1007\/s11042-020-09777-7","journal-title":"Multimed Tools Appl"},{"key":"1478_CR22","doi-asserted-by":"publisher","first-page":"7275","DOI":"10.1007\/s00521-019-04256-z","volume":"32","author":"X Wang","year":"2020","unstructured":"Wang X, Yan WQ (2020) Cross-view gait recognition through ensemble learning. Neural Comput Applic 32:7275\u20137287. https:\/\/doi.org\/10.1007\/s00521-019-04256-z","journal-title":"Neural Comput Applic"},{"issue":"2","key":"1478_CR23","doi-asserted-by":"publisher","first-page":"757","DOI":"10.1016\/j.jksuci.2023.01.014","volume":"35","author":"A Mohammed","year":"2023","unstructured":"Mohammed A, Kora R (2023) A comprehensive review on ensemble deep learning: opportunities and challenges. J King Saud Univ - Comput Inf Sci 35(2):757\u2013774. https:\/\/doi.org\/10.1016\/j.jksuci.2023.01.014","journal-title":"J King Saud Univ - Comput Inf Sci"},{"issue":"1","key":"1478_CR24","doi-asserted-by":"publisher","first-page":"154","DOI":"10.1109\/LSP.2015.2507200","volume":"23","author":"I Rida","year":"2015","unstructured":"Rida I, Jiang X, Marcialis GL (2015) Human body part selection by group Lasso of motion for model-free gait recognition. IEEE Signal Process Lett 23(1):154\u2013158. https:\/\/doi.org\/10.1109\/LSP.2015.2507200","journal-title":"IEEE Signal Process Lett"},{"key":"1478_CR25","doi-asserted-by":"crossref","unstructured":"Rokanujjaman M, Hossain MA, Islam MR (2012) Effective part selection for part-based gait identification, In 2012 7th IEEE International Conference on Electrical and Computer Engineering, (pp. 17\u201319)","DOI":"10.1109\/ICECE.2012.6471473"},{"key":"1478_CR26","doi-asserted-by":"publisher","first-page":"8237","DOI":"10.1007\/s11042-017-4712-z","volume":"77","author":"A Ghebleh","year":"2018","unstructured":"Ghebleh A, Ebrahimi Moghaddam M (2018) Clothing-invariant human gait recognition using an adaptive outlier detection method. Multimed Tools Appl 77:8237\u20138257. https:\/\/doi.org\/10.1007\/s11042-017-4712-z","journal-title":"Multimed Tools Appl"},{"key":"1478_CR27","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 Proc. 18th Int. Conf. Pattern Recog. (pp. 441\u2013444)"},{"key":"1478_CR28","doi-asserted-by":"crossref","unstructured":"Tan D, Huang K, Yu S, Tan T (2006) Efficient night gait recognition based on template matching, In 18th international conference on pattern recognition ICPR\u201906 (p. 3:1000\u20131003)","DOI":"10.1109\/ICPR.2006.478"},{"key":"1478_CR29","doi-asserted-by":"publisher","first-page":"106988","DOI":"10.1016\/j.patcog.2019.106988","volume":"96","author":"C Song","year":"2019","unstructured":"Song C, Huang Y, Huang Y, Jia N, Wang L (2019) Gaitnet: an end-to-end network for gait based human identification. Pattern Recognit 96:106988. https:\/\/doi.org\/10.1016\/j.patcog.2019.106988","journal-title":"Pattern Recognit"},{"key":"1478_CR30","doi-asserted-by":"crossref","unstructured":"Liu Z, Mao H, Wu CY, Feichtenhofer C, Darrell T, Xie S (2022) A convnet for the 2020s, In Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition (pp. 11976\u201311986)","DOI":"10.1109\/CVPR52688.2022.01167"},{"key":"1478_CR31","doi-asserted-by":"publisher","first-page":"83403","DOI":"10.1007\/s11042-024-18859-9","volume":"83","author":"B Yaprak","year":"2024","unstructured":"Yaprak B, Gedikli E (2024) Different gait combinations based on multi-modal deep CNN architectures. Multimed. Tools Appl 83:83403\u201383425. https:\/\/doi.org\/10.1007\/s11042-024-18859-9","journal-title":"Tools Appl"},{"key":"1478_CR32","doi-asserted-by":"publisher","first-page":"228","DOI":"10.1016\/j.patcog.2019.04.023","volume":"93","author":"Y Zhang","year":"2019","unstructured":"Zhang Y, Huang Y, Wang L, Yu S (2019) A comprehensive study on gait biometrics using a joint CNN-based method. Pattern Recognit 93:228\u2013236. https:\/\/doi.org\/10.1016\/j.patcog.2019.04.023","journal-title":"Pattern Recognit"},{"key":"1478_CR33","doi-asserted-by":"crossref","unstructured":"Zhang Z, Tran L, Yin X, Atoum Y, Liu X, Wan J, Wang N (2019) Gait recognition via disentangled representation learning, In Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition (pp. 4710\u20134719)","DOI":"10.1109\/CVPR.2019.00484"},{"key":"1478_CR34","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, In Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition (pp. 1569\u20131577)","DOI":"10.1109\/CVPRW56347.2022.00163"},{"issue":"8","key":"1478_CR35","doi-asserted-by":"publisher","first-page":"3455","DOI":"10.1007\/s00371-023-02973-0","volume":"39","author":"S Gao","year":"2023","unstructured":"Gao S, Tan Z, Ning J, Hou B, Li L (2023) ResGait: gait feature refinement based on residual structure for gait recognition. Vis Comput 39(8):3455\u20133466. https:\/\/doi.org\/10.1007\/s00371-023-02973-0","journal-title":"Vis Comput"},{"key":"1478_CR36","doi-asserted-by":"publisher","first-page":"3265","DOI":"10.1109\/TMM.2021.3095809","volume":"24","author":"K Xu","year":"2021","unstructured":"Xu K, Jiang X, Sun T (2021) Gait recognition based on local graphical skeleton descriptor with pairwise similarity network. IEEE Trans Multimedia 24:3265\u20133275. https:\/\/doi.org\/10.1109\/TMM.2021.3095809","journal-title":"IEEE Trans Multimedia"},{"key":"1478_CR37","doi-asserted-by":"publisher","first-page":"12106","DOI":"10.1007\/s11227-023-05143-0","volume":"79","author":"C Meng","year":"2023","unstructured":"Meng C, He X, Tan Z, Luan L (2023) Gait recognition based on 3D human body reconstruction and multi-granular feature fusion. J Supercomput 79:12106\u201312125. https:\/\/doi.org\/10.1007\/s11227-023-05143-0","journal-title":"J Supercomput"},{"key":"1478_CR38","doi-asserted-by":"publisher","first-page":"104710","DOI":"10.1016\/j.compbiomed.2021.104710","volume":"136","author":"SM Usman","year":"2021","unstructured":"Usman SM, Khalid S, Bashir S (2021) A deep learning based ensemble learning method for epileptic Seizur prediction. Comput Biol Med 136:104710. https:\/\/doi.org\/10.1016\/j.compbiomed.2021.104710","journal-title":"Comput Biol Med"},{"key":"1478_CR39","doi-asserted-by":"publisher","first-page":"115819","DOI":"10.1016\/j.eswa.2021.115819","volume":"187","author":"J Kazmaier","year":"2022","unstructured":"Kazmaier J, Van Vuuren JH (2022) The power of ensemble learning in sentiment analysis. Expert Syst Appl 187:115819. https:\/\/doi.org\/10.1016\/j.eswa.2021.115819","journal-title":"Expert Syst Appl"},{"issue":"19","key":"1478_CR40","doi-asserted-by":"publisher","first-page":"10975","DOI":"10.3390\/app131910975","volume":"13","author":"H Ye","year":"2023","unstructured":"Ye H, Sun T, Xu K (2023) Gait recognition based on gait optical flow network with inherent feature pyramid. Appl Sci 13(19):10975. https:\/\/doi.org\/10.3390\/app131910975","journal-title":"Appl Sci"},{"key":"1478_CR41","doi-asserted-by":"publisher","first-page":"7221","DOI":"10.1007\/s00371-024-03426-y","volume":"40","author":"J Xiong","year":"2024","unstructured":"Xiong J, Zou S, Tang J, Tjahjadi T (2024) MCDGait: multimodal co-learning distillation network with spatial-temporal graph reasoning for gait recognition in the wild. Vis Comput 40:7221\u20137234. https:\/\/doi.org\/10.1007\/s00371-024-03426-y","journal-title":"Vis Comput"},{"key":"1478_CR42","doi-asserted-by":"publisher","DOI":"10.1186\/s41074-018-0039-6","author":"N Takemura","year":"2018","unstructured":"Takemura N, Makihara Y, Muramatsu D, Echigo T, Yagi Y (2018) IPSJ Trans Comput Vis Appl 10:1\u201314. https:\/\/doi.org\/10.1186\/s41074-018-0039-6. Multi-view large population gait dataset and its performance evaluation for cross-view gait recognition"},{"key":"1478_CR43","doi-asserted-by":"crossref","unstructured":"Ekinci M, Gedikli E (2005) A novel approach on silhouette based human motion analysis for gait recognition. In International Symposium on Visual Computing (pp. 219\u2013226)","DOI":"10.1007\/11595755_27"},{"issue":"4","key":"1478_CR44","doi-asserted-by":"publisher","first-page":"e1249","DOI":"10.1002\/widm.1249","volume":"8","author":"O Sagi","year":"2018","unstructured":"Sagi O, Rokach L (2018) Ensemble learning: A survey. Wiley Interdiscip Rev Data Min Knowl Discov 8(4):e1249. https:\/\/doi.org\/10.1002\/widm.1249","journal-title":"Wiley Interdiscip Rev Data Min Knowl Discov"},{"key":"1478_CR45","doi-asserted-by":"publisher","first-page":"185","DOI":"10.1007\/978-3-030-48849-9_12","volume-title":"Emotion and information processing","author":"A Gupta","year":"2020","unstructured":"Gupta A, Semwal VB (2020) Multiple task human gait analysis and identification: ensemble learning approach. In: Mohanty SN (ed) Emotion and information processing. Springer, Cham, pp 185\u2013197. https:\/\/doi.org\/10.1007\/978-3-030-48849-9_12"},{"issue":"11","key":"1478_CR46","doi-asserted-by":"publisher","first-page":"12256","DOI":"10.1007\/s11227-021-03768-7","volume":"77","author":"VB Semwal","year":"2021","unstructured":"Semwal VB, Gupta A, Lalwani P (2021) An optimized hybrid deep learning model using ensemble learning approach for human walking activities recognition. J Supercomput 77(11):12256\u201312279. https:\/\/doi.org\/10.1007\/s11227-021-03768-7","journal-title":"J Supercomput"},{"key":"1478_CR47","doi-asserted-by":"publisher","first-page":"9387","DOI":"10.1109\/TIP.2020.2998275","volume":"29","author":"M Ye","year":"2020","unstructured":"Ye M, Lan X, Leng Q, Shen J (2020) Cross-modality person re-identification via modality-aware collaborative ensemble learning. IEEE Trans Image Process 29:9387\u20139399. https:\/\/doi.org\/10.1109\/TIP.2020.2998275","journal-title":"IEEE Trans Image Process"},{"key":"1478_CR48","doi-asserted-by":"crossref","unstructured":"Li Z, Shi Y, Ling H, Chen J, Wang Q, Zhou F (2022), June Reliability exploration with self-ensemble learning for domain adaptive person re-identification, In Proceedings of the AAAI conference on artificial intelligence, pp. 1527\u20131535","DOI":"10.1609\/aaai.v36i2.20043"},{"key":"1478_CR49","doi-asserted-by":"crossref","unstructured":"Liang D, Fan G, Lin G, Chen W, Pan X, Zhu H (2019) Three-stream convolutional neural network with multi-task and ensemble learning for 3d action recognition, In Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition workshops (pp. 0\u20130)","DOI":"10.1109\/CVPRW.2019.00123"},{"issue":"7","key":"1478_CR50","doi-asserted-by":"publisher","first-page":"1044","DOI":"10.1109\/LSP.2018.2841649","volume":"25","author":"Y Xu","year":"2018","unstructured":"Xu Y, Cheng J, Wang L, Xia H, Liu F, Tao D (2018) Ensemble one-dimensional Convolution neural networks for skeleton-based action recognition. IEEE Signal Process Lett 25(7):1044\u20131048","journal-title":"IEEE Signal Process Lett"},{"issue":"2","key":"1478_CR51","doi-asserted-by":"publisher","first-page":"2023","DOI":"10.1007\/s10489-021-02484-2","volume":"52","author":"L Zhao","year":"2022","unstructured":"Zhao L, Guo L, Zhang R, Xie X, Ye X (2022) MmGaitSet: multimodal based gait recognition for countering carrying and clothing changes. Appl Intell 52(2):2023\u20132036. https:\/\/doi.org\/10.1007\/s10489-021-02484-2","journal-title":"Appl Intell"},{"key":"1478_CR52","doi-asserted-by":"publisher","first-page":"108453","DOI":"10.1016\/j.patcog.2021.108453","volume":"124","author":"H Li","year":"2022","unstructured":"Li H, Qiu Y, Zhao H, Zhan J, Chen R, Wei T, Huang Z (2022) GaitSlice: A gait recognition model based on spatio-temporal slice features. Pattern Recognit 124:108453. https:\/\/doi.org\/10.1016\/j.patcog.2021.108453","journal-title":"Pattern Recognit"},{"issue":"1","key":"1478_CR53","doi-asserted-by":"publisher","first-page":"124","DOI":"10.1109\/TBIOM.2020.3031470","volume":"3","author":"A Sepas-Moghaddam","year":"2020","unstructured":"Sepas-Moghaddam A, Etemad A (2020) View-invariant gait recognition with attentive recurrent learning of partial representations. IEEE Trans Biom Behav Identity Sci 3(1):124\u2013137","journal-title":"IEEE Trans Biom Behav Identity Sci"},{"key":"1478_CR54","doi-asserted-by":"crossref","unstructured":"Sepas-Moghaddam A, Ghorbani S, Troje NF, Etemad A (2021) Gait recognition using multi-scale partial representation transformation with capsules. In 2020 IEEE 25th international conference on pattern recognition (ICPR) (pp. 8045\u20138052)","DOI":"10.1109\/ICPR48806.2021.9412517"},{"key":"1478_CR55","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 Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (pp. 22076\u201322085)","DOI":"10.1109\/CVPR52729.2023.02114"},{"key":"1478_CR56","doi-asserted-by":"publisher","first-page":"118920","DOI":"10.1016\/j.ins.2023.03.145","volume":"636","author":"J Chen","year":"2023","unstructured":"Chen J, Wang Z, Zheng C, Zeng K, Zou Q, Cui L (2023) GaitAMR: Cross-view gait recognition via aggregated multi-feature representation. Inf Sci 636:118920. https:\/\/doi.org\/10.1016\/j.ins.2023.03.145","journal-title":"Inf Sci"},{"key":"1478_CR57","doi-asserted-by":"publisher","first-page":"124250","DOI":"10.1016\/j.eswa.2024.124250","volume":"252","author":"T Wei","year":"2024","unstructured":"Wei T, Liu M, Zhao H, Li H (2024) Gmsn: an efficient multi-scale feature extraction network for gait recognition. Expert Syst Appl 252:124250. https:\/\/doi.org\/10.1016\/j.eswa.2024.124250","journal-title":"Expert Syst Appl"},{"issue":"23","key":"1478_CR58","doi-asserted-by":"publisher","first-page":"12476","DOI":"10.1007\/s10489-024-05837-9","volume":"54","author":"X Pan","year":"2024","unstructured":"Pan X, Xie H, Zhang N, Li S (2024) GaitLRDF: gait recognition via local relevant feature representation and discriminative feature learning. Appl Intell 54(23):12476\u201312491. https:\/\/doi.org\/10.1007\/s10489-024-05837-9","journal-title":"Appl Intell"},{"issue":"8","key":"1478_CR59","doi-asserted-by":"publisher","first-page":"8889","DOI":"10.1007\/s10462-022-10365-4","volume":"56","author":"A Parashar","year":"2023","unstructured":"Parashar A, Parashar A, Ding W, Shekhawat RS, Rida I (2023) Deep learning pipelines for recognition of gait biometrics with covariates: a comprehensive review. Artif Intell Rev 56(8):8889\u20138953. https:\/\/doi.org\/10.1007\/s10462-022-10365-4","journal-title":"Artif Intell Rev"},{"key":"1478_CR60","doi-asserted-by":"publisher","first-page":"315","DOI":"10.1016\/j.neucom.2022.07.002","volume":"505","author":"A Parashar","year":"2022","unstructured":"Parashar A, Shekhawat RS, Ding W, Rida I (2022) Intra-class variations with deep learning-based gait analysis: A comprehensive survey of covariates and methods. Neurocomput 505:315\u2013338. https:\/\/doi.org\/10.1016\/j.neucom.2022.07.002","journal-title":"Neurocomput"},{"key":"1478_CR61","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. In Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition (pp. 9707\u20139716)","DOI":"10.1109\/CVPR52729.2023.00936"},{"issue":"12","key":"1478_CR62","doi-asserted-by":"publisher","first-page":"17606","DOI":"10.1007\/s11227-024-06089-7","volume":"80","author":"S Wei","year":"2024","unstructured":"Wei S, Liu W, Wei F, Wang C, Xiong NN (2024) Gaitdlf: global and local fusion for skeleton-based gait recognition in the wild. J Supercomput 80(12):17606\u201317632. https:\/\/doi.org\/10.1007\/s11227-024-06089-7","journal-title":"J Supercomput"},{"issue":"8","key":"1478_CR63","doi-asserted-by":"publisher","first-page":"6154","DOI":"10.1007\/s10489-024-05422-0","volume":"54","author":"G Chen","year":"2024","unstructured":"Chen G, Chen X, Zheng C, Wang J, Liu X, Han Y (2024) Spatiotemporal smoothing aggregation enhanced multi-scale residual deep graph convolutional networks for skeleton-based gait recognition. Appl Intell 54(8):6154\u20136174. https:\/\/doi.org\/10.1007\/s10489-024-05422-0","journal-title":"Appl Intell"},{"issue":"16","key":"1478_CR64","doi-asserted-by":"publisher","first-page":"2458","DOI":"10.3390\/math12162458","volume":"12","author":"M Bilal","year":"2024","unstructured":"Bilal M, Jianbiao H, Mushtaq H, Asim M, Ali G, ElAffendi M (2024) GaitSTAR: Spatial\u2013Temporal Attention-Based Feature-Reweighting architecture for human gait recognition. Mathematics 12(16):2458","journal-title":"Mathematics"}],"container-title":["Pattern Analysis and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10044-025-01478-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10044-025-01478-x\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10044-025-01478-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,7,2]],"date-time":"2025-07-02T16:41:25Z","timestamp":1751474485000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10044-025-01478-x"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,4,30]]},"references-count":64,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2025,6]]}},"alternative-id":["1478"],"URL":"https:\/\/doi.org\/10.1007\/s10044-025-01478-x","relation":{"references":[{"id-type":"doi","id":"10.1186\/s41074-018-0039-6","asserted-by":"subject"}]},"ISSN":["1433-7541","1433-755X"],"issn-type":[{"value":"1433-7541","type":"print"},{"value":"1433-755X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,4,30]]},"assertion":[{"value":"15 February 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"16 April 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"30 April 2025","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 conflicts of interest to declare that are relevant to the content of this article.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}],"article-number":"98"}}