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Micro-Expression Recognition by Regression Model and Group Sparse Spatio-Temporal Feature Learning Ping LU Wenming ZHENG Ziyan WANG Qiang LI Yuan ZONG Minghai XIN Lenan WU Publication IEICE TRANSACTIONS on Information and Systems Vol.E99-D No.6 pp.1694-1697 Publication Date: 2016/06/01 Publicized: 2016/02/29 Online ISSN: 1745-1361 DOI: 10.1587/transinf.2015EDL8221 Type of Manuscript: LETTER Category: Pattern Recognition Keyword: micro-expression recognition, local binary patterns on three orthogonal planes (LBP-TOP), group sparse least squares regression (GSLSR), Full Text: PDF(304.8KB)>>
Summary: In this letter, a micro-expression recognition method is investigated by integrating both spatio-temporal facial features and a regression model. To this end, we first perform a multi-scale facial region division for each facial image and then extract a set of local binary patterns on three orthogonal planes (LBP-TOP) features corresponding to divided facial regions of the micro-expression videos. Furthermore, we use GSLSR model to build the linear regression relationship between the LBP-TOP facial feature vectors and the micro expressions label vectors. Finally, the learned GSLSR model is applied to the prediction of the micro-expression categories for each test micro-expression video. Experiments are conducted on both CASME II and SMIC micro-expression databases to evaluate the performance of the proposed method, and the results demonstrate that the proposed method is better than the baseline micro-expression recognition method. | ||||||||||
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