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Gaze behavior is an important non-verbal cue in social signal processing andhuman-computer interaction. In this paper, we tackle the problem of person- andhead pose-independent 3D gaze estimation from remote cameras, using amulti-modal recurrent convolutional neural network (CNN). We propose to combineface, eyes region, and face landmarks as individual streams in a CNN toestimate gaze in still images. Then, we exploit the dynamic nature of gaze byfeeding the learned features of all the frames in a sequence to a many-to-onerecurrent module that predicts the 3D gaze vector of the last frame. Ourmulti-modal static solution is evaluated on a wide range of head poses and gazedirections, achieving a significant improvement of 14.6% over the state of theart on EYEDIAP dataset, further improved by 4% when the temporal modality isincluded.

Recurrent CNN for 3D Gaze Estimation using Appearance and Shape Cues