Abstract

Facial micro-expressions are involuntary facial expressions with low intensity and short duration natures in which hidden emotions can be revealed. Micro-expression analysis has been increasingly received tremendous attention and become advanced in the field of computer vision. However, it appears to be very challenging and requires resources to a greater extent to study micro-expressions. Most of the recent works have attempted to improve the spontaneous facial micro-expression recognition with sophisticated and hand-crafted feature extraction techniques. The use of deep neural networks has also been adopted to leverage this task. In this paper, we present a compact framework where a rank pooling concept called dynamic image is employed as a descriptor to extract informative features on certain regions of interests along with a convolutional neural network (CNN) deployed on elicited dynamic images to recognize micro-expressions therein. Particularly, facial motion magnification technique is applied on input sequences to enhance the magnitude of facial movements in the data. Subsequently, rank pooling is implemented to attain dynamic images. Only a fixed number of localized facial areas are extracted on the dynamic images based on observed dominant muscular changes. CNN models are fit to the final feature representation for emotion classification task. The framework is simple compared to that of other findings, yet the logic behind it justifies the effectiveness by the experimental results we achieved throughout the study. The experiment is evaluated on three state-of-the-art databases CASMEII, SMIC and SAMM.