Abstract

Our work examines the link between head motion entrainment of interacting couples and human expert's judgment on certain overall behavioral characteristics (e.g., Blame patterns). We employ a data-driven model that clusters head motion in an unsupervised manner into elementary types called kinemes. We propose three groups of similarity measures based on Kullback-Leibler divergence to model entrainment. We find that the divergence of the (joint) distribution of kinemes yields consistent and significant correlation with target behavior characteristics. The divergence of the conditional distribution of kinemes is shown to predict the polarity of the behavioral characteristics. We partly explain the strong correlations via associating the conditional distributions with the prominent behavioral implications of their respective associated kinemes. These results show the possibility of inferring human behavioral …

Date

2015

Authors

Bo Xiao, Panayiotis Georgiou, Brian Baucom, Shrikanth Narayanan

Conference

2015 International Conference on Affective Computing and Intelligent Interaction (ACII)

Pages

91-97

Publisher

IEEE