Towards mimicry recognition during human interactions: Automatic feature selection and representation

Abstract

During face-to-face interpersonal interaction people have a tendency to mimic each other, that is, they change their own behaviors to adjust to the behavior expressed by a partner. In this paper we describe how behavioral information expressed between two interlocutors can be used to detect and identify mimicry and improve recognition of interrelationship and affect between them in a conversation. To automatically analyze how to extract and integrate this behavioral information into a mimicry detection framework for improving affective computing, this paper addresses the main challenge: mimicry representation in terms of optimal behavioral feature extraction and automatic integration. © 2012 ICST Institute for Computer Science, Social Informatics and Telecommunications Engineering.