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Mutual gaze detection, i.e., predicting whether or not two people are lookingat each other, plays an important role in understanding human interactions. Inthis work, we focus on the task of image-based mutual gaze detection, andpropose a simple and effective approach to boost the performance by using anauxiliary 3D gaze estimation task during the training phase. We achieve theperformance boost without additional labeling cost by training the 3D gazeestimation branch using pseudo 3D gaze labels deduced from mutual gaze labels.By sharing the head image encoder between the 3D gaze estimation and the mutualgaze detection branches, we achieve better head features than learned bytraining the mutual gaze detection branch alone. Experimental results on threeimage datasets show that the proposed approach improves the detectionperformance significantly without additional annotations. This work alsointroduces a new image dataset that consists of 33.1K pairs of humans annotatedwith mutual gaze labels in 29.2K images.

Boosting Image-based Mutual Gaze Detection using Pseudo 3D Gaze