A fixed-point rotation-based feature selection method for micro-expression recognition

Micro-expressions (MEs) express spontaneous, subtle, and hard to hide real human emotions. Compared with macro expressions, the occurrence of MEs is characterized by a small number of activated muscles, short duration, and low amplitude of action. Therefore, extracting the sparse spatio-temporal features of MEs is a challenge for ME recognition. In this paper, we try to extract the low-dimensional features of MEs while ensuring a high accuracy. Firstly, considering that ME samples may be inconsistent in the time domain, a differential energy image method is improved to fix the temporal variation of MEs to unit time. An integral projection method is then used to improve the information density. Secondly, a fixed-point rotation-based feature selection method is proposed further select features with large motion variations. Specifically, the features are transformed from RGB to rotation axes in 3D space, and a fixed point is rotated separately to form a point set. The relative position of the points is changed by adjusting the rotation angle thus optimizing the distribution of the point set. The subset of points with large rotation angles is selected as the feature for classification. Finally, the effectiveness of the method is evaluated using SVM as a classifier experimented on three datasets. The experimental results show that the low-dimensional features can perform well for ME recognition.