Abstract

This paper presents a computational study of head motion in human interaction, notably of its role in conveying interlocutors' behavioral characteristics. Head motion is physically complex and carries rich information; current modeling approaches based on visual signals, however , are still limited in their ability to adequately capture these important properties. Guided by the methodology of kinesics , we propose a data-driven approach to identify typical head motion patterns. The approach follows the steps of first segmenting motion events, then parametrically representing the motion by linear predictive features, and finally generalizing the motion types using Gaussian mixture models. The proposed approach is experimentally validated using video recordings of communication sessions from real couples involved in a couples therapy study. In particular we use the head motion model to classify binarized expert …

Date

2015

Authors

Bo Xiao, Panayiotis Georgiou, Brian Baucom, Shrikanth S Narayanan

Journal

IEEE transactions on multimedia

Volume

17

Issue

7

Pages

1107-1119

Publisher

IEEE