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