Cross-Database Micro-Expression Recognition Exploiting Intradomain Structure

Abstract

Micro-expressions are unconscious, faint, short-lived expressions that appear on the faces. It can make people's understanding of psychological state and emotion more accurate. Therefore, micro-expression recognition is particularly important in psychotherapy and clinical diagnosis, which has been widely studied by researchers for the past decades. In practical applications, the micro-expression recognition samples used in training and testing are from different databases, which causes the feature distribution between the training and testing samples to be different to a large extent, resulting in a drastic decrease in the performance of the traditional micro-expression recognition methods. However, most of the existing cross-database micro-expression recognition methods require a large number of model selection or hyperparameter tuning to select better results from them, which consumes a large amount of time and labor costs. In this paper, we overcome this problem by exploiting the intradomain structure. Nonparametric transfer features are learned through intradomain alignment, while at the same time, a classifier is learned through intradomain programming. In order to evaluate the performance, a large number of cross-database experiments were conducted in CASMEII and SMIC databases. The comparison of results shows that this method can achieve a promising recognition accuracy and with high computational efficiency.