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Specifically, we construct a gait dataset that includes 10,307 subjects (5114 males and 5193 females) from 14 view angles ranging 0\u00b0 \u221290\u00b0, 180\u00b0 \u2212270\u00b0.<\/jats:p>\n                  <jats:p>In addition, we evaluate various approaches to gait recognition which are robust against view angles. By using our dataset, we can fully exploit a state-of-the-art method requiring a large number of training samples, e.g., CNN-based cross-view gait recognition method, and we validate effectiveness of such a family of the methods.<\/jats:p>","DOI":"10.1186\/s41074-018-0039-6","type":"journal-article","created":{"date-parts":[[2018,2,20]],"date-time":"2018-02-20T10:58:04Z","timestamp":1519124284000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":340,"title":["Multi-view large population gait dataset and its performance evaluation for cross-view gait recognition"],"prefix":"10.1186","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1977-4690","authenticated-orcid":false,"given":"Noriko","family":"Takemura","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yasushi","family":"Makihara","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Daigo","family":"Muramatsu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tomio","family":"Echigo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yasushi","family":"Yagi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2018,2,20]]},"reference":[{"issue":"4","key":"39_CR1","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. 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