Micro-expression Cognition and Emotion Modeling Based on Gross Reappraisal Strategy

Abstract

Micro-expression cognition is a vital useful input to develop affective computing strategies in modern human-computer/robot interaction. In this paper, an effective system for micro-expression cognition and emotional regulation is described. As input, a micro-expressional face is represented as a point in a 3D space characterized by arousal, valence and stance factors. The capture and recognition method of micro-expressions is based on a novel combination of 3D-Gradient projection descriptor, multi-scale and multi-direction Gabor filter bank and the gradient magnitude weighted Nearest Neighbor Algorithm (NNA) in facial feature regions. The main distinguishing feature of our work is that the emotional regulation model does not simply provide the classification and jump in terms of a set of discrete emotional labels, but that it operates in a continuous 3D emotional space enabling a wide range of intermediary emotional states to be obtained. The micro-expression recognition method has been tested with the Yale University’s facial database and universal participants’ facial database so that it is capable of analyzing any adult subject, male or female in the typical database and interactive process. Then the cognition and emotion system has been applied to the human-robot interaction, and the results are very encouraging and show that our micro-expression cognition and emotion model is generally consistent with human brain emotional regulation mechanisms.