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Whereas existing databases for gait recognition include at most 4007 subjects, we constructed an extremely large-scale gait database that includes 62,528 subjects, with an equal distribution of males and females, and ages ranging from 2 to 95 years old. Moreover, whereas existing gait databases consider a few predefined CO positions on a subject\u2019s body, we constructed a database that contained unconstrained variations of COs being carried in unconstrained positions. Additionally, gait samples were manually classified into seven carrying status (CS) labels. The extremely large-scale gait database enabled us to evaluate recognition performance under cooperative and uncooperative settings, the impact of the training data size, the recognition difficulty level of the CS labels, and the possibility of the classification of CS labels. Particularly, the latter two performance evaluations have not been investigated in previous gait recognition studies.<\/jats:p>","DOI":"10.1186\/s41074-018-0041-z","type":"journal-article","created":{"date-parts":[[2018,5,30]],"date-time":"2018-05-30T13:07:53Z","timestamp":1527685673000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":55,"title":["The OU-ISIR Large Population Gait Database with real-life carried object and its performance evaluation"],"prefix":"10.1186","volume":"10","author":[{"given":"Md. 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