Outlier Detection for Spotting Micro-expressions

Abstract

Facial expression, as a basic communication method, is an important way of emotion expression and cognition. Facial emotional expression impairment seriously affects interpersonal communication and social life. Micro-expressions (MEs) are involuntary and instant facial dynamics that occurs when the subject failed to suppress their genuine emotions, especially in high-stake situations. Psychological research has shown that MEs can reflect people’s true emotions, which is of great help to the treatment of Facial emotional expression impairment. ME spotting aims to locate the apex frame positions of MEs from long videos, which is the first step in ME analysis. Unlike previous researches that used binary classification or maximum feature difference for analysis, in this paper, we apply the idea of outlier detection to spot ME for the first time. MEs are unusual facial dynamics whose movement patterns diverge from others, so they can be regarded as outliers in the feature space of long videos. Our proposed method uses Gaussian model to estimate the probability density function and locates outliers by analyzing the statistical features of long videos to achieve ME spotting. This method was evaluated on CASME I, CASME II and SAMM datasets that only include spontaneous MEs in long videos. The results show that this method can efficiently locate apex frames of ME efficiently in long videos and also provide a new perspective for ME spotting.