%0 Conference Proceedings
%T Recurrent CNN for 3D Gaze Estimation using Appearance and Shape Cues
%A Cristina Palmero
%A Javier Selva
%A Mohammad Ali Bagheri
%A Sergio Escalera
%B 29th British Machine Vision Conference
%D 2018
%F Cristina Palmero2018
%O HUPBA; no proj
%O exported from refbase (http://refbase.cvc.uab.es/show.php?record=3208), last updated on Fri, 12 Jul 2024 14:33:04 +0200
%X 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.
%U http://refbase.cvc.uab.es/files/PSB2018.pdf