# Gaze Estimation for Assisted Living Environments

arXiv: https://arxiv.org/abs/1909.09225 · HTML: https://mlsanity.com/paper/1909.09225/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Sep 2019.

## Built on by (most cited first, 12 of 12)

- [Appearance-based Gaze Estimation With Deep Learning: A Review and Benchmark](https://mlsanity.com/paper/2104.12668/) (2021)
- [Gaze Estimation using Transformer](https://mlsanity.com/paper/2105.14424/) (2021)
- [PureGaze: Purifying Gaze Feature for Generalizable Gaze Estimation](https://mlsanity.com/paper/2103.13173/) (2021)
- [Automatic Gaze Analysis: A Survey of Deep Learning based Approaches](https://mlsanity.com/paper/2108.05479/) (2021)
- [Patch-level Gaze Distribution Prediction for Gaze Following](https://mlsanity.com/paper/2211.11062/) (2022)
- [DVGaze: Dual-View Gaze Estimation](https://mlsanity.com/paper/2308.10310/) (2023)
- [HHP-Net: A light Heteroscedastic neural network for Head Pose estimation with uncertainty](https://mlsanity.com/paper/2111.01440/) (2021)
- [Multi-view Gaze Target Estimation](https://mlsanity.com/paper/2508.05857/) (2025)
- [Suppressing Uncertainty in Gaze Estimation](https://mlsanity.com/paper/2412.12890/) (2024)
- [Confidence-aware 3D Gaze Estimation and Evaluation Metric](https://mlsanity.com/paper/2303.10062/) (2023)
- [Cross-Dataset Gaze Estimation by Evidential Inter-intra Fusion](https://mlsanity.com/paper/2409.04766/) (2024)
- [GazeD: Context-Aware Diffusion for Accurate 3D Gaze Estimation](https://mlsanity.com/paper/2601.12948/) (2026)

## Abstract (arXiv)

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.