Abstract

Gesture dynamic pattern is an essential indicator of emotions or attitudes during human communication. However, there might exist great variability of gesture dynamics among gesture sequences within the same emotion, which form a major obstacle to detect emotion from body motion in general interpersonal interactions. In this paper, we propose a graph-based framework for modeling gesture dynamics towards attitude recognition. We demonstrate that the dynamics derived from a weighted graph based method provide a better separation between distinct emotion classes and maintain less variability within the same emotion class. This helps capture salient dynamic patterns for specific emotions by removing interaction-dependent variations. In this framework, we represent each gesture sequence as an undirected graph of connected gesture units and use the graph-based transform to generate features to …

Date

October 27, 2014

Authors

Zhaojun Yang, Antonio Ortega, Shrikanth Narayanan

Conference

2014 IEEE International Conference on Image Processing (ICIP)

Pages

1515-1519

Publisher

IEEE