{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,17]],"date-time":"2025-11-17T02:59:24Z","timestamp":1763348364361,"version":"3.37.3"},"reference-count":50,"publisher":"Springer Science and Business Media LLC","issue":"28-29","license":[{"start":{"date-parts":[[2021,6,19]],"date-time":"2021-06-19T00:00:00Z","timestamp":1624060800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,6,19]],"date-time":"2021-06-19T00:00:00Z","timestamp":1624060800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Multimed Tools Appl"],"published-print":{"date-parts":[[2021,11]]},"DOI":"10.1007\/s11042-021-10941-w","type":"journal-article","created":{"date-parts":[[2021,6,19]],"date-time":"2021-06-19T08:03:00Z","timestamp":1624089780000},"page":"36033-36058","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Reduction of covariate factors from Silhouette image for robust gait recognition"],"prefix":"10.1007","volume":"80","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3171-9926","authenticated-orcid":false,"given":"Sanjay Kumar","family":"Gupta","sequence":"first","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,6,19]]},"reference":[{"issue":"2","key":"10941_CR1","doi-asserted-by":"publisher","first-page":"397","DOI":"10.1109\/TCDS.2017.2658674","volume":"10","author":"H Aggarwal","year":"2018","unstructured":"Aggarwal H, Vishwakarma DK (2018) Covariate conscious approach for gait recognition based upon zernike moment invariants. IEEE Trans Cogn Dev Syst 10(2):397\u2013407","journal-title":"IEEE Trans Cogn Dev Syst"},{"key":"10941_CR2","doi-asserted-by":"publisher","first-page":"103","DOI":"10.1016\/j.cviu.2017.10.004","volume":"164","author":"M Alotaibi","year":"2017","unstructured":"Alotaibi M, Mahmood A (2017) Improved gait recognition based on specialized deep convolutional neural network. Comput Vis Image Underst 164:103\u2013110","journal-title":"Comput Vis Image Underst"},{"doi-asserted-by":"crossref","unstructured":"Ariyanto G, Nixon MS (2011) Model-based 3d gait biometrics. In: 2011 international joint conference on biometrics (IJCB), pp. 1\u20137, IEEE","key":"10941_CR3","DOI":"10.1109\/IJCB.2011.6117582"},{"issue":"13","key":"10941_CR4","doi-asserted-by":"publisher","first-page":"2052","DOI":"10.1016\/j.patrec.2010.05.027","volume":"31","author":"K Bashir","year":"2010","unstructured":"Bashir K, Xiang T, Gong S (2010) Gait recognition without subject cooperation. Pattern Recogn Lett 31(13):2052\u20132060","journal-title":"Pattern Recogn Lett"},{"issue":"8","key":"10941_CR5","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 Vis Comput 27(8):1194\u20131206","journal-title":"Image Vis Comput"},{"issue":"4","key":"10941_CR6","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"},{"doi-asserted-by":"crossref","unstructured":"Francisco M. Castro,\u00a0Manuel J. Mar\u00edn-Jim\u00e9nez,\u00a0Nicol\u00e1s Guil\u00a0&\u00a0Nicol\u00e1s P\u00e9rez de la Blanca (2020) Multimodal feature fusion for cnn-based gait recognition: an empirical comparison. Neural Computing Applications 32, 14173\u201314193","key":"10941_CR7","DOI":"10.1007\/s00521-020-04811-z"},{"issue":"1","key":"10941_CR8","doi-asserted-by":"publisher","first-page":"53","DOI":"10.1016\/j.jvcir.2013.02.010","volume":"25","author":"P Chattopadhyay","year":"2014","unstructured":"Chattopadhyay P, Roy A, Sural S, Mukhopadhyay J (2014) Pose depth volume extraction from rgb-d streams for frontal gait recognition. J Vis Commun Image Represent 25(1):53\u201363","journal-title":"J Vis Commun Image Represent"},{"issue":"6","key":"10941_CR9","doi-asserted-by":"publisher","first-page":"751","DOI":"10.1109\/THMS.2017.2706658","volume":"47","author":"P Chaurasia","year":"2017","unstructured":"Chaurasia P, Yogarajah P, Condell J, Prasad G (2017) Fusion of random walk and discrete fourier spectrum methods for gait recognition. IEEE Trans Human-Mach Syst 47(6):751\u2013762","journal-title":"IEEE Trans Human-Mach Syst"},{"issue":"11","key":"10941_CR10","doi-asserted-by":"publisher","first-page":"977","DOI":"10.1016\/j.patrec.2009.04.012","volume":"30","author":"C Chen","year":"2009","unstructured":"Chen C, Liang J, Zhao H, Hu H, Tian J (2009) Frame difference energy image for gait recognition with incomplete silhouettes. Pattern Recognit Let 30(11):977\u2013984","journal-title":"Pattern Recognit Let"},{"doi-asserted-by":"crossref","unstructured":"de Lima VC, Schwartz WR (2019) Gait recognition using pose estimation and signal processing. Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications, CIARP\u00a02019 (pp. 719\u2013728). Springer","key":"10941_CR11","DOI":"10.1007\/978-3-030-33904-3_68"},{"doi-asserted-by":"crossref","unstructured":"Deng M, Wang C (2019) Human gait recognition based on deterministic learning and data stream of microsoft kinect. IEEE Transactions on Circuits and Systems for Video Technology 29(12):3636\u20133645","key":"10941_CR12","DOI":"10.1109\/TCSVT.2018.2883449"},{"unstructured":"Discriminant analysis (2019) https:\/\/in.mathworks.com\/help\/stats\/classify.html","key":"10941_CR13"},{"unstructured":"Goodfellow I, Pouget-Abadie J, Mirza M, Xu B, Warde-Farley D, Ozair S, Courville A, Bengio Y(2014) Generative adversarial nets. In: NIPS'14: Proceedings of the 27th International Conference on Neural Information Processing Systems, pp 2672\u20132680","key":"10941_CR14"},{"issue":"7","key":"10941_CR15","doi-asserted-by":"publisher","first-page":"1521","DOI":"10.1109\/TPAMI.2014.2366766","volume":"37","author":"Y Guan","year":"2015","unstructured":"Guan Y, Li C-T, Roli F (2015) On reducing the effect of covariate factors in gait recognition: a classifier ensemble method. IEEE Trans Pattern Anal Mach Intel 37(7):1521\u20131528","journal-title":"IEEE Trans Pattern Anal Mach Intel"},{"doi-asserted-by":"crossref","unstructured":"Gupta SK, Chattopadhyay P (2020) Exploiting pose dy-namics for human recognition from their gait signatures. Multimed Tools Appl pp. 1\u201319","key":"10941_CR16","DOI":"10.1007\/s11042-020-10071-9"},{"doi-asserted-by":"crossref","unstructured":"Gupta SK, Chattopadhyay P (2021) Gait Recognition in the Presence of Co-variate Conditions. Neurocomputing, vol. 454, pp. 76\u201387, 2021.","key":"10941_CR17","DOI":"10.1016\/j.neucom.2021.04.113"},{"doi-asserted-by":"crossref","unstructured":"Gupta SK, Sultaniya GM, Chattopadhyay P (2020) An Efficient Descriptor for Gait Recognition Using Spatio-Temporal Cues. In: Mandal J, Bhattacharya D (eds) Emerging Technology in Modelling and Graphics. Advances in Intelligent Systems and Computing, vol 937. Springer, Singapore.","key":"10941_CR18","DOI":"10.1007\/978-981-13-7403-6_10"},{"issue":"2","key":"10941_CR19","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(2):316\u2013322","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"issue":"1","key":"10941_CR20","doi-asserted-by":"publisher","first-page":"195","DOI":"10.1016\/j.jvcir.2013.02.006","volume":"25","author":"M Hofmann","year":"2014","unstructured":"Hofmann M, Geiger J, Bachmann S, Schuller B, Rigoll G (2014) 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","journal-title":"J Vis Commun Image Represent"},{"issue":"6","key":"10941_CR21","doi-asserted-by":"publisher","first-page":"2281","DOI":"10.1016\/j.patcog.2009.12.020","volume":"43","author":"MA Hossain","year":"2010","unstructured":"Hossain MA, Makihara Y, Wang J, Yagi Y (2010) Clothing-invariant gait identification using part-based clothing categorization and adaptive weight control. Pattern Recogn 43(6):2281\u20132291","journal-title":"Pattern Recogn"},{"issue":"4","key":"10941_CR22","doi-asserted-by":"publisher","first-page":"2256","DOI":"10.1109\/TIP.2011.2180914","volume":"21","author":"X Huang","year":"2012","unstructured":"Huang X, Boulgouris NV (2012) Gait recognition with shifted energy image and structural feature extraction. IEEE Trans Image Process 21(4):2256\u20132268","journal-title":"IEEE Trans Image Process"},{"doi-asserted-by":"crossref","unstructured":"Isola P, Zhu J-Y, Zhou T, Efros AA (2017) Image-to-image translation with conditional adversarial networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 1125\u20131134","key":"10941_CR23","DOI":"10.1109\/CVPR.2017.632"},{"issue":"1","key":"10941_CR24","doi-asserted-by":"publisher","first-page":"119","DOI":"10.1109\/TIFS.2017.2738611","volume":"13","author":"N Khamsemanan","year":"2018","unstructured":"Khamsemanan N, Nattee C, Jianwattanapaisarn N (2018) Human identification from freestyle walks using posture-based gait feature. IEEE Trans Inf Forensics Secur 13(1):119\u2013128","journal-title":"IEEE Trans Inf Forensics Secur"},{"issue":"5","key":"10941_CR25","doi-asserted-by":"publisher","first-page":"956","DOI":"10.1109\/TFUZZ.2018.2870590","volume":"27","author":"P Kumar","year":"2018","unstructured":"Kumar P, Mukherjee S, Saini R, Kaushik P, Roy PP, Dogra DP (2018) Multimodal gait recognition with inertial sensor data and video using evolutionary algorithm. IEEE Trans Fuzzy Syst 27(5):956\u2013965","journal-title":"IEEE Trans Fuzzy Syst"},{"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. In: 2009 IEEE 12th international conference on computer vision workshops, ICCV workshops, (pp. 1058\u20131064), IEEE","key":"10941_CR26","DOI":"10.1109\/ICCVW.2009.5457587"},{"doi-asserted-by":"crossref","unstructured":"Lam TH, Lee RS (2006) A new representation for human gait recognition: motion silhouettes image (msi). In: International conference on biometrics, (pp. 612\u2013618). Springer","key":"10941_CR27","DOI":"10.1007\/11608288_81"},{"issue":"4","key":"10941_CR28","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 Recogn 44(4):973\u2013987","journal-title":"Pattern Recogn"},{"issue":"4","key":"10941_CR29","doi-asserted-by":"publisher","first-page":"638","DOI":"10.1007\/s12555-009-0414-2","volume":"7","author":"H Lee","year":"2009","unstructured":"Lee H, Hong S, Nizami IF, Kim E (2009) A noise robust gait representation: motion energy image. Int J Control Autom Syst 7(4):638\u2013643","journal-title":"Int J Control Autom Syst"},{"doi-asserted-by":"crossref","unstructured":"Liu J, Zheng N (2007) Gait history image: a novel temporal template for gait recognition. In: 2007 IEEE international conference on multimedia and expo, pp. 663\u2013666, IEEE","key":"10941_CR30","DOI":"10.1109\/ICME.2007.4284737"},{"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. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5705\u20135715","key":"10941_CR31","DOI":"10.1109\/CVPR.2017.718"},{"issue":"1","key":"10941_CR32","doi-asserted-by":"publisher","first-page":"140","DOI":"10.1109\/TIP.2014.2371335","volume":"24","author":"D Muramatsu","year":"2014","unstructured":"Muramatsu D, Shiraishi A, Makihara Y, Uddin MZ, Yagi Y (2014) Gait-based person recognition using arbitrary view transformation model. IEEE Trans Image Process 24(1):140\u2013154","journal-title":"IEEE Trans Image Process"},{"issue":"3","key":"10941_CR33","doi-asserted-by":"publisher","first-page":"780","DOI":"10.1016\/j.sigpro.2011.09.022","volume":"92","author":"A Roy","year":"2012","unstructured":"Roy A, Sural S, Mukherjee J (2012) Gait recognition using pose kinematics and pose energy image. Signal Process 92(3):780\u2013792","journal-title":"Signal Process"},{"issue":"458","key":"10941_CR34","doi-asserted-by":"publisher","first-page":"657","DOI":"10.1198\/jasa.2002.s479","volume":"97","author":"JR Schott","year":"2002","unstructured":"Schott JR (2002) Principles of multivariate analysis: a user\u2019s perspective (rev. ed.).(book reviews). J Am Stat Assoc 97(458):657\u2013659","journal-title":"J Am Stat Assoc"},{"doi-asserted-by":"publisher","unstructured":"Seber GA (2009) Multivariate observations, vol. 252. John Wiley & Sons,\u00a0Book Series: Wiley Series in Probability and Statistics. https:\/\/doi.org\/10.1002\/9780470316641","key":"10941_CR35","DOI":"10.1002\/9780470316641"},{"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: 2016 international conference on biometrics (ICB), pp. 1\u20138, IEEE","key":"10941_CR36","DOI":"10.1109\/ICB.2016.7550060"},{"doi-asserted-by":"crossref","unstructured":"Sivapalan S, Chen D, Denman S, Sridharan S, Fookes C (2011) Gait energy volumes and frontal gait recognition using depth images. In: biometrics (IJCB), 2011 international joint conference on, pp. 1\u20136, IEEE","key":"10941_CR37","DOI":"10.1109\/IJCB.2011.6117504"},{"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), vol. 3, pp. 1000\u20131003, IEEE","key":"10941_CR38"},{"issue":"1","key":"10941_CR39","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1186\/s41074-018-0041-z","volume":"10","author":"MZ Uddin","year":"2018","unstructured":"Uddin MZ, Ngo TT, Makihara Y, Takemura N, Li X, Muramatsu D, Yagi Y (2018) The ou-isir large population gait database with real-life carried object and its performance evaluation. IPSJ Trans Comput Vis Appl 10(1):5","journal-title":"IPSJ Trans Comput Vis Appl"},{"issue":"11","key":"10941_CR40","doi-asserted-by":"publisher","first-page":"2164","DOI":"10.1109\/TPAMI.2011.260","volume":"34","author":"C Wang","year":"2012","unstructured":"Wang C, Zhang J, Wang L, Pu J, Yuan X (2012) 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":"10941_CR41","doi-asserted-by":"publisher","first-page":"107","DOI":"10.1016\/j.patcog.2015.08.011","volume":"50","author":"X Xing","year":"2016","unstructured":"Xing X, Wang K, Yan T, Lv Z (2016) Complete canonical correlation analysis with application to multi-view gait recognition. Pattern Recogn 50:107\u2013117","journal-title":"Pattern Recogn"},{"issue":"8","key":"10941_CR42","doi-asserted-by":"publisher","first-page":"2904","DOI":"10.1016\/j.patcog.2010.03.011","volume":"43","author":"Z Xue","year":"2010","unstructured":"Xue Z, Ming D, Song W, Wan B, Jin S (2010) Infrared gait recognition based on wavelet transform and support vector machine. Pattern Recogn 43(8):2904\u20132910","journal-title":"Pattern Recogn"},{"issue":"9","key":"10941_CR43","doi-asserted-by":"publisher","first-page":"2350","DOI":"10.1016\/j.sigpro.2008.03.006","volume":"88","author":"X Yang","year":"2008","unstructured":"Yang X, Zhou Y, Zhang T, Shu G, Yang J (2008) Gait recognition based on dynamic region analysis. Signal Process 88(9):2350\u20132356","journal-title":"Signal Process"},{"doi-asserted-by":"crossref","unstructured":"Yoo D, Kim N, Park S, Paek AS, Kweon IS (2016) Pixel-level domain transfer. In: European conference on computer vision, (pp. 517\u2013532). Springer","key":"10941_CR44","DOI":"10.1007\/978-3-319-46484-8_31"},{"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: 18th international conference on pattern recognition (ICPR\u201906), vol. 4, pp. 441\u2013444, IEEE","key":"10941_CR45"},{"doi-asserted-by":"crossref","unstructured":"Yu S, Chen H, Reyes G, Edel B, Poh N (2017) Gaitgan: invariant gait feature extraction using generative adversarial networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp. 30\u201337","key":"10941_CR46","DOI":"10.1109\/CVPRW.2017.80"},{"key":"10941_CR47","doi-asserted-by":"publisher","first-page":"81","DOI":"10.1016\/j.neucom.2017.02.006","volume":"239","author":"S Yu","year":"2017","unstructured":"Yu S, Chen H, Wang Q, Shen L, Huang Y (2017) Invariant feature extraction for gait recognition using only one uniform model. Neurocomputing 239:81\u201393","journal-title":"Neurocomputing"},{"doi-asserted-by":"crossref","unstructured":"Yu S, Liao R, An W, Chen H, Garc\u00eda EB, Huang Y, Poh N (2019) GaitGANv2: Invariant gait feature extraction using generative adversarial networks. Pattern Recogn 87:179\u2013189","key":"10941_CR48","DOI":"10.1016\/j.patcog.2018.10.019"},{"doi-asserted-by":"crossref","unstructured":"Zhang E, Zhao Y, Xiong W (2010) Active energy image plus 2DLPP for gait recognition. Signal Process 90(7):2295\u20132302","key":"10941_CR49","DOI":"10.1016\/j.sigpro.2010.01.024"},{"doi-asserted-by":"crossref","unstructured":"Zhang K, Luo W, Ma L, Liu W, Li H (2019) Learning joint gait representation via quintuplet loss minimization. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 4700\u20134709","key":"10941_CR50","DOI":"10.1109\/CVPR.2019.00483"}],"container-title":["Multimedia Tools and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-021-10941-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11042-021-10941-w\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-021-10941-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,11,30]],"date-time":"2021-11-30T17:36:03Z","timestamp":1638293763000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11042-021-10941-w"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,6,19]]},"references-count":50,"journal-issue":{"issue":"28-29","published-print":{"date-parts":[[2021,11]]}},"alternative-id":["10941"],"URL":"https:\/\/doi.org\/10.1007\/s11042-021-10941-w","relation":{},"ISSN":["1380-7501","1573-7721"],"issn-type":[{"type":"print","value":"1380-7501"},{"type":"electronic","value":"1573-7721"}],"subject":[],"published":{"date-parts":[[2021,6,19]]},"assertion":[{"value":"22 June 2020","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"15 December 2020","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"14 April 2021","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"19 June 2021","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}