Gesture dynamics modeling for attitude analysis using graph based transform

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 describe gesture dynamics. In our experiments, we apply the graph-based dynamics for attitude recognition, i.e., classifying the attitude of an individual as friendly or conflictive. Experimental results verify the effectiveness of our approach.