Gaze Estimation for Assisted Living Environments

TL;DR

This paper presents a neural network model for gaze estimation in assisted living environments, using facial keypoints and a confidence gating mechanism, achieving competitive accuracy and reliable uncertainty estimation.

Contribution

The paper introduces a simple neural network regressor with a confidence gated input layer for gaze estimation, capable of estimating uncertainty and handling occlusions.

Findings

Performs on par with complex baselines on public benchmarks.

Outputs highly correlated uncertainty estimates.

Demonstrates effectiveness in real assisted living images.

Abstract

Effective assisted living environments must be able to perform inferences on how their occupants interact with one another as well as with surrounding objects. To accomplish this goal using a vision-based automated approach, multiple tasks such as pose estimation, object segmentation and gaze estimation must be addressed. Gaze direction in particular provides some of the strongest indications of how a person interacts with the environment. In this paper, we propose a simple neural network regressor that estimates the gaze direction of individuals in a multi-camera assisted living scenario, relying only on the relative positions of facial keypoints collected from a single pose estimation model. To handle cases of keypoint occlusion, our model exploits a novel confidence gated unit in its input layer. In addition to the gaze direction, our model also outputs an estimation of its own prediction uncertainty. Experimental results on a public benchmark demonstrate that our approach performs on pair with a complex, dataset-specific baseline, while its uncertainty predictions are highly correlated to the actual angular error of corresponding estimations. Finally, experiments on images from a real assisted living environment demonstrate the higher suitability of our model for its final application.