It’s Written All Over Your Face: Full-Face Appearance-Based Gaze Estimation
Xucong Zhang,Yusuke Sugano,Mario Fritz,Andreas Bulling +3 more
- pp 2299-2308
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TLDR
In this paper, the authors propose an appearance-based method that only takes the full face image as input, which achieves improvements of up to 14.3% on MPIIGaze and 27.7% on EYEDIAP for person-independent gaze estimation.Abstract:
Eye gaze is an important non-verbal cue for human affect analysis. Recent gaze estimation work indicated that information from the full face region can benefit performance. Pushing this idea further, we propose an appearance-based method that, in contrast to a long-standing line of work in computer vision, only takes the full face image as input. Our method encodes the face image using a convolutional neural network with spatial weights applied on the feature maps to flexibly suppress or enhance information in different facial regions. Through extensive evaluation, we show that our full-face method significantly outperforms the state of the art for both 2D and 3D gaze estimation, achieving improvements of up to 14.3% on MPIIGaze and 27.7% on EYEDIAP for person-independent 3D gaze estimation. We further show that this improvement is consistent across different illumination conditions and gaze directions and particularly pronounced for the most challenging extreme head poses.read more
Citations
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Journal ArticleDOI
MPIIGaze: Real-World Dataset and Deep Appearance-Based Gaze Estimation
TL;DR: GazeNet as discussed by the authors proposes a deep appearance-based gaze estimation method for unconstrained gaze estimation from a monocular RGB camera without assumptions regarding user, environment, or camera.
Book ChapterDOI
RT-GENE: Real-Time Eye Gaze Estimation in Natural Environments
TL;DR: This work addresses the issue of ground truth annotation by measuring head pose using a motion capture system and eye gaze using mobile eyetracking glasses and applies semantic image inpainting to the area covered by the glasses to bridge the gap between training and testing images by removing the obtrusiveness of the glasses.
Posted Content
MPIIGaze: Real-World Dataset and Deep Appearance-Based Gaze Estimation
TL;DR: It is shown that image resolution and the use of both eyes affect gaze estimation performance, while head pose and pupil centre information are less informative, and GazeNet is proposed, the first deep appearance-based gaze estimation method.
Proceedings ArticleDOI
Gaze360: Physically Unconstrained Gaze Estimation in the Wild
TL;DR: Gaze360 as discussed by the authors is a large-scale remote gaze tracking dataset and method for robust 3D gaze estimation in unconstrained images, which consists of 238 subjects in indoor and outdoor environments with labelled three-dimensional (3D) gaze across a wide range of head poses and distances.
Proceedings ArticleDOI
Learning to find eye region landmarks for remote gaze estimation in unconstrained settings
TL;DR: This work presents a novel learning-based method for eye region landmark localization that enables conventional methods to be competitive to latest appearance-based methods and exceeds the state of the art for iris localization and eye shape registration on real-world imagery.
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