PT Unknown
AU Cristina Palmero
   Javier Selva
   Mohammad Ali Bagueri
   Sergio Escalera
TI Recurrent CNN for 3D Gaze Estimation using Appearance and Shape Cues
BT 29th British Machine Vision Conference
PY 2018
AB Gaze behavior is an important non-verbal cue in social signal processing and humancomputer interaction. In this paper, we tackle the problem of person- and head poseindependent 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 onEYEDIAP dataset, further improved by 4% when the temporal modality is included.
ER