Abstract:

Different from ordinary facial expression, micro-expression has the characteristics of short duration andlow intensity, which is often difficult to be identified effectively, resulting in the limitation of research in this field. Dealing with these difficulties, a novel feature combination method was proposed. Calculation using the global optical flow technology was carried out in the adjacent frames to obtain faint optical flow. The motion information of each two adjacent frames was passed by, so that the changes were significant between the images of two frames at a distance, solving the problem of short duration and weak action. The optical flow feature and space-temporal local texture feature extracted by LBP-TOP(Local Binary Patterns from Three Orthogonal Planes) operator were combined to make supplement for describing most region details of the faces. The random forest classifier was selected, and experimental results show that the two features have a very good complementary, in the CASMEII database. The method is able to identify five types of emotion, the accuracy is promoted from 40. 50% to 64. 46%, and the category discrimination is improved accordingly.

Key words: micro-expression, optical flow, local binary patterns from three orthogonal planes(LBP-TOP);
random forest