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

2018-09-17 11:09:42
Cristina Palmero, Javier Selva, Mohammad Ali Bagheri, Sergio Escalera

Abstract

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.

Abstract (translated)

URL

https://arxiv.org/abs/1805.03064

PDF

https://arxiv.org/pdf/1805.03064


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