{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,12]],"date-time":"2026-06-12T16:52:43Z","timestamp":1781283163318,"version":"3.54.1"},"reference-count":43,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2022,5,4]],"date-time":"2022-05-04T00:00:00Z","timestamp":1651622400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2022,5,4]],"date-time":"2022-05-04T00:00:00Z","timestamp":1651622400000},"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":["SIViP"],"published-print":{"date-parts":[[2023,6]]},"DOI":"10.1007\/s11760-022-02217-z","type":"journal-article","created":{"date-parts":[[2022,5,4]],"date-time":"2022-05-04T19:04:02Z","timestamp":1651691042000},"page":"925-936","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["FCML-gait: fog computing and machine learning inspired human identity and gender recognition using gait sequences"],"prefix":"10.1007","volume":"17","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4774-5582","authenticated-orcid":false,"given":"Khalil","family":"Ahmed","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Munish","family":"Saini","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2022,5,4]]},"reference":[{"key":"2217_CR1","doi-asserted-by":"crossref","unstructured":"Reid, D.A., Nixon, M.S.: Imputing human descriptions in semantic biometrics. In: Proceedings of the 2nd ACM workshop on Multimedia in Forensics, Security &Intelligence, pp. 25\u201330 (2010).","DOI":"10.1145\/1877972.1877982"},{"key":"2217_CR2","unstructured":"Agrafioti, F., Bui, F.M., Hatzinakos, D.: Enabling continuous or instantaneous identity recognition of a large group of people based on physiological biometric signals obtained from members of a small group of people. ed: Google Patents (2017)."},{"issue":"1","key":"2217_CR3","first-page":"281","volume":"3","author":"A Joshi","year":"2014","unstructured":"Joshi, A., Bhushan, S., Kaur, M.J.: Gait Recognition of human using SVM and BPNN classifiers. Int. J. Comput. Sci. Mobile Comput. 3(1), 281\u2013290 (2014)","journal-title":"Int. J. Comput. Sci. Mobile Comput."},{"issue":"6","key":"2217_CR4","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3394713","volume":"53","author":"E Ellavarason","year":"2020","unstructured":"Ellavarason, E., Guest, R., Deravi, F., Sanchez-Riello, R., Corsetti, B.: Touch-dynamics based behavioural biometrics on mobile devices\u2013a review from a usability and performance perspective. ACM Comput. Surveys (CSUR) 53(6), 1\u201336 (2020)","journal-title":"ACM Comput. Surveys (CSUR)"},{"issue":"4","key":"2217_CR5","first-page":"85","volume":"5","author":"K Ahmed","year":"2017","unstructured":"Ahmed, K., Doegar, A.: A review on human identity and gender recognition from gait sequences. Int. J. Res. Electron. Comput. Eng. 5(4), 85\u201391 (2017)","journal-title":"Int. J. Res. Electron. Comput. Eng."},{"issue":"21","key":"2217_CR6","doi-asserted-by":"publisher","first-page":"7991","DOI":"10.1016\/j.eswa.2015.06.016","volume":"42","author":"R Arroyo","year":"2015","unstructured":"Arroyo, R., Yebes, J.J., Bergasa, L.M., Daza, I., Almaz\u00e1n, J.: Expert video-surveillance system for real-time detection of suspicious behaviors in shopping malls. Expert Syst. Appl. 42(21), 7991\u20138005 (2015)","journal-title":"Expert Syst. Appl."},{"issue":"3","key":"2217_CR7","doi-asserted-by":"publisher","first-page":"315","DOI":"10.15837\/ijccc.2016.3.2556","volume":"11","author":"I Buciu","year":"2016","unstructured":"Buciu, I., Gacsadi, A.: Biometrics systems and technologies: a survey. Int. J. Comput. Commun. Control 11(3), 315\u2013330 (2016)","journal-title":"Int. J. Comput. Commun. Control"},{"issue":"2","key":"2217_CR8","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/2933241","volume":"49","author":"RD Labati","year":"2016","unstructured":"Labati, R.D., Genovese, A., Mu\u00f1oz, E., Piuri, V., Scotti, F., Sforza, G.: Biometric recognition in automated border control: a survey. ACM Comput. Surveys (CSUR) 49(2), 1\u201339 (2016)","journal-title":"ACM Comput. Surveys (CSUR)"},{"issue":"1","key":"2217_CR9","doi-asserted-by":"publisher","first-page":"51","DOI":"10.1109\/TIFS.2013.2291969","volume":"9","author":"J Lu","year":"2013","unstructured":"Lu, J., Wang, G., Moulin, P.: Human identity and gender recognition from gait sequences with arbitrary walking directions. IEEE Trans. Inf. Forensics Secur. 9(1), 51\u201361 (2013)","journal-title":"IEEE Trans. Inf. Forensics Secur."},{"key":"2217_CR10","doi-asserted-by":"crossref","unstructured":"Jeevan, M., Jain, N., Hanmandlu, M., Chetty, G.: Gait recognition based on gait pal and pal entropy image. In: IEEE International Conference on Image Processing (2013).","DOI":"10.1109\/ICIP.2013.6738864"},{"key":"2217_CR11","doi-asserted-by":"publisher","first-page":"530","DOI":"10.1016\/j.neucom.2015.07.012","volume":"173","author":"X Zhao","year":"2016","unstructured":"Zhao, X., Jiang, Y., Stathaki, T., Zhang, H.: Gait recognition method for arbitrary straight walking paths using appearance conversion machine. Neurocomputing 173, 530\u2013540 (2016)","journal-title":"Neurocomputing"},{"key":"2217_CR12","doi-asserted-by":"publisher","first-page":"396","DOI":"10.1016\/j.jvcir.2016.03.020","volume":"38","author":"DL Fern\u00e1ndez","year":"2016","unstructured":"Fern\u00e1ndez, D.L., MadridCuevas, F.J., Poyato, A.C., Mu\u00f1oz-Salinas, R., Carnicer, R.M.: A new approach for multi-view gait recognition on unconstrained paths. J. Vis. Commun. Image Represent. 38, 396\u2013406 (2016)","journal-title":"J. Vis. Commun. Image Represent."},{"issue":"2","key":"2217_CR13","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.: Cross-view gait recognition by fusion of multiple transformation consistency measures. IET Biometrics 4(2), 62\u201373 (2015)","journal-title":"IET Biometrics"},{"key":"2217_CR14","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.patrec.2016.05.009","volume":"80","author":"SD Choudhury","year":"2016","unstructured":"Choudhury, S.D., Tjahjadi, T.: Clothing and carrying condition invariant gait recognition based on rotation forest. Pattern Recogn. Lett. 80, 1\u20137 (2016)","journal-title":"Pattern Recogn. Lett."},{"issue":"7","key":"2217_CR15","doi-asserted-by":"publisher","first-page":"1697","DOI":"10.1109\/TPAMI.2017.2726061","volume":"40","author":"X Chen","year":"2017","unstructured":"Chen, X., Weng, J., Lu, W., Xu, J.: Multi-gait recognition based on attribute discovery. IEEE Trans. Pattern Anal. Mach. Intell. 40(7), 1697\u20131710 (2017)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"2217_CR16","doi-asserted-by":"crossref","unstructured":"Marcin, D.: Human gait recognition based on ground reaction forces in case of sport shoes and high heels. In: 2017 IEEE International Conference on INnovations in Intelligent SysTems and Applications (INISTA), pp. 247\u2013252 (2017).","DOI":"10.1109\/INISTA.2017.8001165"},{"key":"2217_CR17","doi-asserted-by":"crossref","unstructured":"Fengjiang, C., Muqing, D., Cong, W.: Kinect-based gait recognition system design via deterministic learning.In: 29th Chinese Control and Decision Conference (CCDC), pp. 5916\u20135921 (2017).","DOI":"10.1109\/CCDC.2017.7978227"},{"key":"2217_CR18","doi-asserted-by":"crossref","unstructured":"Kumar, M., Singh, N., Kumar, R., Goel, S., Kumar, K.: Gait recognition based on vision systems: a systematic survey. J. Vis. Commun. Image Represent. (2021).","DOI":"10.1016\/j.jvcir.2021.103052"},{"issue":"2","key":"2217_CR19","doi-asserted-by":"publisher","first-page":"369","DOI":"10.1007\/s11760-018-1365-y","volume":"13","author":"MH Khan","year":"2019","unstructured":"Khan, M.H., Farid, M.S., Grzegorzek, M.: Spatiotemporal features of human motion for gait recognition. SIViP 13(2), 369\u2013377 (2019)","journal-title":"SIViP"},{"key":"2217_CR20","doi-asserted-by":"crossref","unstructured":"Hidalgo, M.N., Pastor, F.J.F., Sarabia, R.J.V., Pascual, J.M., Chamizo, J.M.G.: Gait analysis using computer vision based on cloud platform and mobile device. Hindawi Mobile Inf. Syst. Ambient Assisted Living Ambient Intell. Health 2018 (2018).","DOI":"10.1155\/2018\/7381264"},{"key":"2217_CR21","doi-asserted-by":"publisher","first-page":"101784","DOI":"10.1109\/ACCESS.2020.2998412","volume":"8","author":"S Batool","year":"2020","unstructured":"Batool, S., Hassan, A., Saqib, N.A., Khattak, M.A.K.: Authentication of remote IoT users based on deeper gait analysis of sensor data. IEEE Access 8, 101784\u2013101796 (2020)","journal-title":"IEEE Access"},{"key":"2217_CR22","doi-asserted-by":"crossref","unstructured":"Elharrouss, O., Almaadeed, N., Al-Maadeed, S., Bouridane, A.: Gait recognition for person re-identification. J. Supercomput. 1\u201320 (2020).","DOI":"10.1007\/s11227-020-03409-5"},{"issue":"9","key":"2217_CR23","doi-asserted-by":"publisher","first-page":"2629","DOI":"10.1109\/TBME.2019.2893528","volume":"66","author":"A Seifert","year":"2019","unstructured":"Seifert, A., Amin, M.G., Zoubir, A.M.: Toward unobtrusive in-home gait analysis based on radar micro-Doppler signatures. IEEE Trans. Biomed. Eng. 66(9), 2629\u20132640 (2019)","journal-title":"IEEE Trans. Biomed. Eng."},{"issue":"1","key":"2217_CR24","doi-asserted-by":"publisher","first-page":"119","DOI":"10.1109\/TIFS.2017.2738611","volume":"13","author":"N Khamsemanan","year":"2017","unstructured":"Khamsemanan, N., Nattee, C., Jianwattanapaisarn, N.: Human identification from freestyle walks using posture-based gait feature. IEEE Trans. Inf. Forensics Secur. 13(1), 119\u2013128 (2017)","journal-title":"IEEE Trans. Inf. Forensics Secur."},{"key":"2217_CR25","unstructured":"\u201cNeural Networks For Non-Intrusive Biometric Recognition,\u201d http:\/\/www.advancedsourcecode.com\/gaitrecognition.asp. Accessed on 4 June 2020."},{"issue":"1","key":"2217_CR26","doi-asserted-by":"publisher","first-page":"14","DOI":"10.1049\/iet-bmt.2018.5063","volume":"8","author":"I Rida","year":"2018","unstructured":"Rida, I., Almaadeed, N., Almaadeed, S.: Robust gait recognition: a comprehensive survey. IET Biometrics 8(1), 14\u201328 (2018)","journal-title":"IET Biometrics"},{"key":"2217_CR27","unstructured":"Khachikian, S., Emadi, M.: A review of detector descriptors\u2019 on object tracking. Electronic Resource (2016)."},{"key":"2217_CR28","unstructured":"Gao, J., Huang, X., Peng, G., Wang, M., Wu, Z.: Simplified SIFT feature point detecting method. Appl. Res. Comput. 7 (2008)."},{"key":"2217_CR29","doi-asserted-by":"crossref","unstructured":"Bay, H., Ess, A., Tuytelaars, T., Gool, V.: Speeded-up robust features (SURF). Computer vision and image understanding (CVIU). In: Proceedings of the 9th European conference on computer vision. Springer, Austria (2006).","DOI":"10.1007\/11744023_32"},{"key":"2217_CR30","unstructured":"Pedersen, J. T.: Study group SURF: feature detection & description. In Department of Computer Science, Aarhus University (2011)."},{"issue":"3","key":"2217_CR31","doi-asserted-by":"publisher","first-page":"346","DOI":"10.1016\/j.cviu.2007.09.014","volume":"110","author":"H Bay","year":"2008","unstructured":"Bay, H., Ess, A., Tuytelaar, T., Gool, L.V.: Speeded-up robust features (SURF). Comput. Vis. Image Underst. 110(3), 346\u2013359 (2008)","journal-title":"Comput. Vis. Image Underst."},{"key":"2217_CR32","doi-asserted-by":"crossref","unstructured":"Bansal, M., Kumar, M., Sachdeva, M., Mittal, A.: Transfer learning for image classification using VGG19: Caltech-101. J. Ambient Intell. Hum. Comput. (2021).","DOI":"10.1007\/s12652-021-03488-z"},{"key":"2217_CR33","doi-asserted-by":"crossref","unstructured":"Bansal, M., Kumar, M., Kumar, M.: 2D object recognition: a comparative analysis of SIFT,SURF and ORB feature descriptors. Multimedia Tools Appl. (2021).","DOI":"10.1007\/s11042-021-10646-0"},{"key":"2217_CR34","doi-asserted-by":"crossref","unstructured":"Gupta, S., Thakur, K., Kumar, M.: 2D-human face recognition using SIFT and SURF descriptors of face\u2019s feature regions. Vis. Comput. Int. J. Comput. Graph. (2020).","DOI":"10.1007\/s00371-020-01814-8"},{"key":"2217_CR35","first-page":"458","volume-title":"Application of Support Vector Machines in a Life Assurance Environment","author":"SJ Steel","year":"2005","unstructured":"Steel, S.J., Hechter, G.K.: Application of Support Vector Machines in a Life Assurance Environment, pp. 458\u2013465. Springer, Berlin (2005)"},{"key":"2217_CR36","doi-asserted-by":"crossref","unstructured":"Pradhan, N., Singh, A. S.: Machine learning architecture and framework. In: Machine Learning and Cognitive Computing for Mobile Communications and Wireless Networks, pp. 1\u201324. Wiley, Hoboken (2020).","DOI":"10.1002\/9781119640554.ch1"},{"key":"2217_CR37","doi-asserted-by":"crossref","unstructured":"Sharma, S., Lone, F. R., Lone, M. R.: Machine learning for enhancement of security in internet of things based applications. In: Security and Privacy in the Internet of Things. Chapman and Hall\/CRC, Boca Raton (2020).","DOI":"10.1201\/9781003016304-7"},{"key":"2217_CR38","doi-asserted-by":"crossref","unstructured":"Saini, A., Singh, H.: Enhanced human identity and gender recognition from gait sequences using SVM and MDA. Int. J. Comput. Appl. 119(2) (2015).","DOI":"10.5120\/21037-3358"},{"key":"2217_CR39","doi-asserted-by":"crossref","unstructured":"Ng, H., Tong, H., Tan, W., & Abdullah, J.: Gait classification by support vector machine. Commun. Comput. Inf. Sci. 623\u2013636 (2011).","DOI":"10.1007\/978-3-642-22170-5_54"},{"issue":"2","key":"2217_CR40","first-page":"67","volume":"2","author":"E Byvatov","year":"2003","unstructured":"Byvatov, E., Schneider, G.: Support vector machine applications in bioinformatics. Appl. Bioinform. 2(2), 67\u201377 (2003)","journal-title":"Appl. Bioinform."},{"issue":"2","key":"2217_CR41","doi-asserted-by":"publisher","first-page":"187","DOI":"10.14257\/ijsh.2016.10.2.18","volume":"10","author":"CS Nandyala","year":"2016","unstructured":"Nandyala, C.S., Kim, H.: From cloud to fog and IoT-based real-time U-healthcare monitoring for smart homes and hospitals. Int. J. Smart Home 10(2), 187\u2013196 (2016)","journal-title":"Int. J. Smart Home"},{"issue":"3","key":"2217_CR42","first-page":"28","volume":"128","author":"A Abdiansah","year":"2015","unstructured":"Abdiansah, A., Wardoyo, R.: Time complexity analysis of support vector machines(SVM) in LibSVM. Int. J. Comput. Appl. 128(3), 28\u201334 (2015)","journal-title":"Int. J. Comput. Appl."},{"key":"2217_CR43","doi-asserted-by":"publisher","first-page":"19753","DOI":"10.1007\/s11042-021-10711-8","volume":"80","author":"S Singh","year":"2021","unstructured":"Singh, S., Ahuja, U., Kumar, M., Kumar, K., Sachdeva, M.: Face mask detection using YOLOv3 and faster R-CNN models: COVID-19 environment. Multimedia Tools Appl. 80, 19753\u201319768 (2021)","journal-title":"Multimedia Tools Appl."}],"container-title":["Signal, Image and Video Processing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11760-022-02217-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11760-022-02217-z\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11760-022-02217-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,4,9]],"date-time":"2025-04-09T12:31:35Z","timestamp":1744201895000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11760-022-02217-z"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,5,4]]},"references-count":43,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2023,6]]}},"alternative-id":["2217"],"URL":"https:\/\/doi.org\/10.1007\/s11760-022-02217-z","relation":{},"ISSN":["1863-1703","1863-1711"],"issn-type":[{"value":"1863-1703","type":"print"},{"value":"1863-1711","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,5,4]]},"assertion":[{"value":"20 February 2021","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"30 January 2022","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"23 March 2022","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"4 May 2022","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}