{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,22]],"date-time":"2026-01-22T17:16:58Z","timestamp":1769102218600,"version":"3.49.0"},"reference-count":40,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2020,10,12]],"date-time":"2020-10-12T00:00:00Z","timestamp":1602460800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2020,10,12]],"date-time":"2020-10-12T00:00:00Z","timestamp":1602460800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["No.61602431"],"award-info":[{"award-number":["No.61602431"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004731","name":"Natural Science Foundation of Zhejiang Province","doi-asserted-by":"publisher","award":["No.Y20F020113"],"award-info":[{"award-number":["No.Y20F020113"]}],"id":[{"id":"10.13039\/501100004731","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Multimed Tools Appl"],"published-print":{"date-parts":[[2021,2]]},"DOI":"10.1007\/s11042-020-09935-x","type":"journal-article","created":{"date-parts":[[2020,10,12]],"date-time":"2020-10-12T07:02:55Z","timestamp":1602486175000},"page":"6065-6078","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":21,"title":["Non-local gait feature extraction and human identification"],"prefix":"10.1007","volume":"80","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1773-9760","authenticated-orcid":false,"given":"Xiuhui","family":"Wang","sequence":"first","affiliation":[]},{"given":"Wei Qi","family":"Yan","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2020,10,12]]},"reference":[{"key":"9935_CR1","unstructured":"Arjovsky M, Chintala S, Bottou L (2017) Wasserstein generative adversarial networks. In: Proceedings of the 34th international conference on machine learning, vol 70"},{"key":"9935_CR2","unstructured":"Chen X, Duan Y, Houthooft R, Schulman J, Sutskever I, Abbeel P (2016) InfoGAN: interpretable representation learning by information maximizing generative adversarial nets. In: Advances in neural information processing systems, vol 29"},{"key":"9935_CR3","doi-asserted-by":"crossref","unstructured":"Chen Q, Wang Y, Liu Z, Liu Q, Huang D (2017) Feature map pooling for cross-view gait recognition based on silhouette sequence images. In: IEEE international joint conference on biometrics (IJCB), pp 54\u201361","DOI":"10.1109\/BTAS.2017.8272682"},{"key":"9935_CR4","unstructured":"Goodfellow I, Pouget-Abadie J, Mirza M, Xu B, Warde-Farley D, Ozair S, Courville A, Bengio Y (2014) Generative adversarial nets. In: Ghahramani Z, Welling M, Cortes C, Lawrence ND, Weinberger KQ (eds) Advances in neural information processing systems 27. Curran Associates, Inc, pp 2672\u20132680"},{"key":"9935_CR5","volume-title":"Deep learning","author":"I Goodfellow","year":"2016","unstructured":"Goodfellow I, Bengio Y, Courville A (2016) Deep learning. MIT Press, Cambridge"},{"key":"9935_CR6","doi-asserted-by":"crossref","unstructured":"Hagui M, Mahjoub MA (2016) Hidden conditional random fields for gait recognition. In: International image processing, applications and systems, pp 1\u20136","DOI":"10.1109\/IPAS.2016.7880139"},{"issue":"02","key":"9935_CR7","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 (2006) Individual recognition using gait energy image. IEEE Trans Pattern Anal Mach Intell 28(02):316\u2013323","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"9935_CR8","first-page":"68","volume":"40","author":"A Hanon AlAsadi","year":"2014","unstructured":"Hanon AlAsadi A (2014) Gait recognition using support vector machine and neural network. J Basrah Res 40:68\u201378","journal-title":"J Basrah Res"},{"issue":"05","key":"9935_CR9","first-page":"442","volume":"31","author":"Y He","year":"2018","unstructured":"He Y, Zhang J (2018) Deep learning for gait recognition: a survey. Pattern Recognit Artif Intell 31(05):442\u2013451","journal-title":"Pattern Recognit Artif Intell"},{"key":"9935_CR10","doi-asserted-by":"crossref","unstructured":"He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: IEEE conference on computer vision and pattern recognition (CVPR), pp 770\u2013778","DOI":"10.1109\/CVPR.2016.90"},{"issue":"5","key":"9935_CR11","doi-asserted-by":"publisher","first-page":"1511","DOI":"10.1109\/TIFS.2012.2204253","volume":"7","author":"H Iwama","year":"2012","unstructured":"Iwama H, Okumura M, Makihara Y, Yagi Y (2012) The OU-ISIR gait database comprising the large population dataset and performance evaluation of gait recognition. IEEE Trans Inf Forensics Secur 7(5):1511\u20131521","journal-title":"IEEE Trans Inf Forensics Secur"},{"key":"9935_CR12","doi-asserted-by":"crossref","unstructured":"Kanwar A, Upadhyay P (2014) An appearance based approach for gait identification using infrared imaging. In: International conference on issues and challenges in intelligent computing techniques (ICICT), pp 719\u2013724","DOI":"10.1109\/ICICICT.2014.6781369"},{"key":"9935_CR13","unstructured":"Kingma D P, Welling M (2014) Auto-encoding variational bayes. In: 2nd International conference on learning representations, vol 1"},{"issue":"10","key":"9935_CR14","doi-asserted-by":"publisher","first-page":"3329","DOI":"10.3390\/s18103329","volume":"18","author":"P Kozlow","year":"2018","unstructured":"Kozlow P, Abid N, Yanushkevich S N (2018) Gait type analysis using dynamic bayesian networks. Sensors 18(10):3329\u20133338","journal-title":"Sensors"},{"key":"9935_CR15","doi-asserted-by":"crossref","unstructured":"Krajushkina A, N\u00f5mm S, Toomela A, Medijainen K, Tamm E, Vaske M, Uvarov D, Kahar H, Nugis M, Taba P (2018) Gait analysis based approach for parkinson\u2019s disease modeling with decision tree classifiers. In: IEEE International conference on systems, man, and cybernetics, vol 10, pp 3720\u20133725","DOI":"10.1109\/SMC.2018.00630"},{"issue":"6","key":"9935_CR16","doi-asserted-by":"publisher","first-page":"84","DOI":"10.1145\/3065386","volume":"60","author":"A Krizhevsky","year":"2017","unstructured":"Krizhevsky A, Sutskever I, Hinton G E (2017) ImageNet classification with deep convolutional neural networks. CACM 60(6):84\u201390","journal-title":"CACM"},{"key":"9935_CR17","doi-asserted-by":"crossref","unstructured":"Kusakunniran W, Wu Q, Li H, Zhang J (2010) Multiple views gait recognition using view transformation model based on optimized gait energy image. In: IEEE International conference on information and automation, pp 1058\u20131064","DOI":"10.1109\/ICCVW.2009.5457587"},{"key":"9935_CR18","doi-asserted-by":"publisher","first-page":"973","DOI":"10.1016\/j.patcog.2010.10.011","volume":"44","author":"T Lam","year":"2011","unstructured":"Lam T, Cheung K H, Liu J (2011) Gait flow image: a silhouette-based gait representation for human identification. Pattern Recognit 44:973\u2013987","journal-title":"Pattern Recognit"},{"issue":"7","key":"9935_CR19","doi-asserted-by":"publisher","first-page":"436","DOI":"10.1038\/nature14539","volume":"521","author":"Y LeCun","year":"2015","unstructured":"LeCun Y, Bengio Y, Hinton G (2015) Deep learning. Nature 521(7):436\u2013445","journal-title":"Nature"},{"key":"9935_CR20","doi-asserted-by":"crossref","unstructured":"Manap HH, Tahir NM, Abdullah R (2012) Anomalous gait detection using naive bayes classifier. In: IEEE symposium on industrial electronics and applications, pp 378\u2013381","DOI":"10.1109\/ISIEA.2012.6496664"},{"key":"9935_CR21","doi-asserted-by":"crossref","unstructured":"Mao X, Li Q, Xie H, Lau RYK, Wang Z, Smolley SP (2017) Least squares generative adversarial networks. In: 2017 IEEE international conference on computer vision (ICCV), pp 2813\u20132821","DOI":"10.1109\/ICCV.2017.304"},{"issue":"2","key":"9935_CR22","doi-asserted-by":"publisher","first-page":"62","DOI":"10.1049\/iet-bmt.2014.0042","volume":"4","author":"D Muramatsu","year":"2015","unstructured":"Muramatsu D, Makihara Y, Yagi Y (2015) Cross-view gait recognition by fusion of multiple transformation consistency measures. IET Biom 4 (2):62\u201373","journal-title":"IET Biom"},{"issue":"7","key":"9935_CR23","doi-asserted-by":"publisher","first-page":"1602","DOI":"10.1109\/TCYB.2015.2452577","volume":"46","author":"D Muramatsu","year":"2016","unstructured":"Muramatsu D, Makihara Y, Yagi Y (2016) View transformation model incorporating quality measures for cross-view gait recognition. IEEE Trans Cybern 46(7):1602\u20131615","journal-title":"IEEE Trans Cybern"},{"key":"9935_CR24","doi-asserted-by":"publisher","first-page":"36322","DOI":"10.1109\/ACCESS.2019.2905015","volume":"7","author":"Z Pan","year":"2019","unstructured":"Pan Z, Yu W, Yi X, Khan A, Yuan F, Zheng Y (2019) Recent progress on generative adversarial networks (GANs): a survey. IEEE Access 7:36322\u201336333","journal-title":"IEEE Access"},{"issue":"C","key":"9935_CR25","doi-asserted-by":"publisher","first-page":"60","DOI":"10.1016\/j.patrec.2016.01.008","volume":"73","author":"R San-Segundo","year":"2016","unstructured":"San-Segundo R, Cordoba R, Ferreiros J, D\u2019Haro-Enr\u00edquez LF (2016) Frequency features and GMM-UBM approach for gait-based person identification using smartphone inertial signals. Pattern Recogn Lett 73(C):60\u201367","journal-title":"Pattern Recogn Lett"},{"issue":"02","key":"9935_CR26","doi-asserted-by":"publisher","first-page":"162","DOI":"10.1109\/TPAMI.2005.39","volume":"27","author":"S Sarkar","year":"2005","unstructured":"Sarkar S, Phillips P, Liu Z (2005) The humanid gait challenge problem: data sets, performance, and analysis. IEEE Trans Pattern Anal Mach Intell 27 (02):162\u2013177","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"9935_CR27","doi-asserted-by":"crossref","unstructured":"Shiraga K, Makihara Y, Muramatsu D, Echigo T, Yagi Y (2016) Geinet: view-invariant gait recognition using a convolutional neural network. In: International conference on biometrics (ICB), vol 1, pp 1\u20138","DOI":"10.1109\/ICB.2016.7550060"},{"key":"9935_CR28","unstructured":"Sonderby CK, Raiko T, Maaloe L, Sonderby S K, Winther O (2016) Ladder variational autoencoders. In: Advances in neural information processing systems, vol 29"},{"issue":"1","key":"9935_CR29","first-page":"1","volume":"1","author":"N Takemura","year":"2018","unstructured":"Takemura N, Makihara Y, Muramatsu D, Echigo T, Yagi Y (2018) On input\/output architectures for convolutional neural network-based cross-view gait recognition. IEEE Trans Circ Syst Video Technol 1(1):1\u20131","journal-title":"IEEE Trans Circ Syst Video Technol"},{"key":"9935_CR30","doi-asserted-by":"publisher","first-page":"57583","DOI":"10.1109\/ACCESS.2018.2874073","volume":"6","author":"S Tong","year":"2018","unstructured":"Tong S, Fu Y, Yue X, Ling H (2018) Multi-view gait recognition based on a spatial-temporal deep neural network. IEEE Access 6:57583\u201357596","journal-title":"IEEE Access"},{"key":"9935_CR31","doi-asserted-by":"crossref","unstructured":"Tsunashima H, Hoshi T, Chen Q (2018) DzGAN: improved conditional generative adversarial nets using divided Z-vector. In: 2018 International conference on computing and big data. International conference on computing and big data, Coll Charleston, Charleston, SC, SEP 08-10, 2018, pp 52\u201355","DOI":"10.1145\/3277104.3277110"},{"key":"9935_CR32","doi-asserted-by":"crossref","unstructured":"Wang X, Yan W Q (2019) Cross-view gait recognition through ensemble learning. In: Neural computing and applications","DOI":"10.1007\/s00521-019-04256-z"},{"issue":"1","key":"9935_CR33","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1142\/S0129065719500278","volume":"30","author":"X Wang","year":"2020","unstructured":"Wang X, Yan W Q (2020) Human gait recognition based on frame-by-frame gait energy images and convolutional long short term memory. Int J Neural Syst 30(1):1\u201312","journal-title":"Int J Neural Syst"},{"issue":"10","key":"9935_CR34","doi-asserted-by":"publisher","first-page":"12545","DOI":"10.1007\/s11042-017-4903-7","volume":"77","author":"X Wang","year":"2018","unstructured":"Wang X, Wang J, Yan K (2018) Gait recognition based on Gabor wavelets and (2D)2PCA. Multimed Tools Appl 77(10):12545\u201312561","journal-title":"Multimed Tools Appl"},{"key":"9935_CR35","doi-asserted-by":"crossref","unstructured":"Wang X, Girshick R, Gupta A, He K (2018) Non-local neural networks. In: IEEE\/CVF conference on computer vision and pattern recognition, pp 7794\u20137803","DOI":"10.1109\/CVPR.2018.00813"},{"key":"9935_CR36","doi-asserted-by":"crossref","unstructured":"Wang X, Feng S, Yan W Q (2019) Human gait recognition based on self-adaptive hidden Markov model. In: IEEE transactions on computational biology and bioinformatics, pp 1\u201310","DOI":"10.1109\/TCBB.2019.2951146"},{"key":"9935_CR37","unstructured":"Wang X, Zhang J, Yan W Q (2019) Gait recognition using multichannel convolution neural networks. In: Neural computing and applications, pp 532\u2013539"},{"issue":"02","key":"9935_CR38","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(02):209\u2013226","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"9935_CR39","doi-asserted-by":"publisher","first-page":"424","DOI":"10.1016\/j.jvcir.2018.06.019","volume":"55","author":"H Wu","year":"2018","unstructured":"Wu H, Weng J, Chen X, Lu W (2018) Feedback weight convolutional neural network for gait recognition. J Vis Commun Image Represent 55:424\u2013432","journal-title":"J Vis Commun Image Represent"},{"key":"9935_CR40","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: International conference on pattern recognition, pp 441\u2013444"}],"container-title":["Multimedia Tools and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-020-09935-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11042-020-09935-x\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-020-09935-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,11,22]],"date-time":"2022-11-22T23:22:33Z","timestamp":1669159353000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11042-020-09935-x"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,10,12]]},"references-count":40,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2021,2]]}},"alternative-id":["9935"],"URL":"https:\/\/doi.org\/10.1007\/s11042-020-09935-x","relation":{},"ISSN":["1380-7501","1573-7721"],"issn-type":[{"value":"1380-7501","type":"print"},{"value":"1573-7721","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,10,12]]},"assertion":[{"value":"22 December 2019","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"15 September 2020","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"17 September 2020","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"12 October 2020","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Compliance with Ethical Standards"}},{"value":"We declare that we have not financial and personal relationships with other people or organizations that can inappropriately influence our work, there is no professional or other personal interest of any nature or kind in any product, service and\/or company that could be construed as influencing the position presented in, or the review of, the manuscript entitled \u201cNon-local Gait Feature Extraction and Human Identification\u201d.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"<!--Emphasis Type='Bold' removed-->Conflict of interest"}}]}}