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Second, dynamic features are obtained by applying LBPs on Three Orthogonal Planes (LBP-TOP) and Eight Vertices LBP (EVLBP) frame by frame. Next, the static and dynamic features are combined to create a 1377-dimensional vector for each video. Finally, Gradient Boosting Regression is used to predict depression scores. The experimental results on the AVEC 2014 depression dataset ([Formula: see text], [Formula: see text]) demonstrate the effectiveness of the proposed method. These results indicate that the low-dimensional vectors extracted by the proposed method can effectively capture the facial motion of individuals with depression, and also suggest that hand-crafted methods could have potential in depression detection. <\/jats:p>","DOI":"10.1142\/s0218126623503115","type":"journal-article","created":{"date-parts":[[2023,4,30]],"date-time":"2023-04-30T08:15:40Z","timestamp":1682842540000},"source":"Crossref","is-referenced-by-count":8,"title":["Depression Detection with Dynamic and Static Visual Features"],"prefix":"10.1142","volume":"32","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0828-1874","authenticated-orcid":false,"given":"Yuhao","family":"Wang","sequence":"first","affiliation":[{"name":"School of Information Science and Engineering, Lanzhou University, Lanzhou, Gansu 730100, P. R. 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