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In this paper, we propose a gait-based age estimation method using convolutional neural networks (CNNs). Because gait features vary depending on a subject\u2019s attributes, i.e., gender and generation, we propose the following three CNN stages: (1) a CNN for gender estimation, (2) a CNN for age-group estimation, and (3) a CNN for age regression. We conducted experiments using a large population gait database and confirm that the proposed method outperforms state-of-the-art benchmarks.<\/jats:p>","DOI":"10.1186\/s41074-019-0054-2","type":"journal-article","created":{"date-parts":[[2019,6,10]],"date-time":"2019-06-10T13:03:06Z","timestamp":1560171786000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":35,"title":["Gait-based age estimation using multi-stage convolutional neural network"],"prefix":"10.1186","volume":"11","author":[{"given":"Atsuya","family":"Sakata","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Noriko","family":"Takemura","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":[[2019,6,10]]},"reference":[{"issue":"10","key":"54_CR1","doi-asserted-by":"publisher","first-page":"2401","DOI":"10.1109\/TPAMI.2013.51","volume":"35","author":"X Geng","year":"2013","unstructured":"Geng X, Yin C, Zhou ZH (2013) Facial age estimation by learning from label distributions. 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