Gaze Pattern Recognition in Dyadic Communication

Abstract

Analyzing gaze behaviors is crucial to interpret the nature of communication. Current studies on gaze have focused primarily on the detection of a single pattern, such as the Looking-At-Each-Other pattern or the shared attention pattern. In this work, we re-define five static gaze patterns that cover all the status during a dyadic communication and propose a network to recognize these mutual exclusive gaze patterns given an image. We annotate a benchmark, called GP-Static, for the gaze pattern recognition task, on which our method experimentally outperforms other alternate solutions. Our method also achieves the state-of-art performance on other two single gaze pattern recognition tasks. The analysis of gaze patterns on preschool children demonstrates that the statistic of the proposed static gaze patterns conforms with the findings in psychology.