Abstract

Body gestures are an important non-verbal expression channel during affective communication. They convey human attitudes and emotions as they dynamically unfold during an interpersonal interaction. Hence, it is highly desirable to understand the dynamics of body gestures associated with emotion expression in human interactions. We present a statistical framework for robustly modeling the dynamics of body gestures in dyadic interactions. Our framework is based on high-level semantic gesture patterns and consists of three components. First, we construct a universal background model (UBM) using Gaussian mixture modeling (GMM) to represent subject-independent gesture variability. Next, we describe each gesture sequence as a concatenation of semantic gesture patterns which are derived from a parallel HMM structure. Then, we probabilistically compare the segments of each gesture sequence …

Date

2016

Authors

Zhaojun Yang, Shrikanth S Narayanan

Journal

IEEE Transactions on Affective Computing

Volume

8

Issue

3

Pages

369-381

Publisher

IEEE