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  

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
micro-expression recognition,  local binary patterns on three orthogonal planes (LBP-TOP),  group sparse least squares regression (GSLSR),  

Full Text: PDF(304.8KB)>>
Buy this Article

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.