Real-time Automatic Emotion Recognition from Body Gestures
This addresses the problem of emotion recognition for applications like interactive games, but it is incremental as it extends existing methods to body gestures.
The paper tackles real-time automatic emotion recognition from body movements, achieving a 61.3% recognition rate on a six-emotion problem, which is close to the 61.9% rate of human observers.
Although psychological research indicates that bodily expressions convey important affective information, to date research in emotion recognition focused mainly on facial expression or voice analysis. In this paper we propose an approach to realtime automatic emotion recognition from body movements. A set of postural, kinematic, and geometrical features are extracted from sequences 3D skeletons and fed to a multi-class SVM classifier. The proposed method has been assessed on data acquired through two different systems: a professionalgrade optical motion capture system, and Microsoft Kinect. The system has been assessed on a "six emotions" recognition problem, and using a leave-one-subject-out cross validation strategy, reached an overall recognition rate of 61.3% which is very close to the recognition rate of 61.9% obtained by human observers. To provide further testing of the system, two games were developed, where one or two users have to interact to understand and express emotions with their body.