Micro-expression recognition for detecting human emotional changes

Abstract

We propose a method estimating human emotional state in communication from four micro-expressions; mouth motion, head pose, eye sight direction, and blinking interval. Those micro-expressions are picked up by a questionnaire survey of human observers watching on video recorded human conversation. Then we implemented a recognition system for those micro-expressions. We detect facial parts from a RGB-Depth camera, measure those four expressions. Then we apply decision-tree style classifier to detect some emotional state and state changes. In our experiment, we gathered 30 videos of human communicating with his/her friend. Then we trained and validated our algorithm with two-fold cross-validation. We compared the classifier output with human examiners’ observation and confirmed over 70% precision.