# It's Written All Over Your Face: Full-Face Appearance-Based Gaze Estimation

arXiv: https://arxiv.org/abs/1611.08860 · HTML: https://mlsanity.com/paper/1611.08860/

## Key facts

- Cited by 123 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Nov 2016.

## Built on by (most cited first, 30 of 123)

- [Appearance-based Gaze Estimation With Deep Learning: A Review and Benchmark](https://mlsanity.com/paper/2104.12668/) (2021)
- [Rank-N-Contrast: Learning Continuous Representations for Regression](https://mlsanity.com/paper/2210.01189/) (2022)
- [Gaze Estimation using Transformer](https://mlsanity.com/paper/2105.14424/) (2021)
- [L2CS-Net: Fine-Grained Gaze Estimation in Unconstrained Environments](https://mlsanity.com/paper/2203.03339/) (2022)
- [PureGaze: Purifying Gaze Feature for Generalizable Gaze Estimation](https://mlsanity.com/paper/2103.13173/) (2021)
- [Generalizing Gaze Estimation with Outlier-guided Collaborative Adaptation](https://mlsanity.com/paper/2107.13780/) (2021)
- [Automatic Gaze Analysis: A Survey of Deep Learning based Approaches](https://mlsanity.com/paper/2108.05479/) (2021)
- [Adaptive Feature Fusion Network for Gaze Tracking in Mobile Tablets](https://mlsanity.com/paper/2103.11119/) (2021)
- [Weakly-Supervised Physically Unconstrained Gaze Estimation](https://mlsanity.com/paper/2105.09803/) (2021)
- [GazeOnce: Real-Time Multi-Person Gaze Estimation](https://mlsanity.com/paper/2204.09480/) (2022)
- [DVGaze: Dual-View Gaze Estimation](https://mlsanity.com/paper/2308.10310/) (2023)
- [Contrastive Representation Learning for Gaze Estimation](https://mlsanity.com/paper/2210.13404/) (2022)
- [End-to-end Video Gaze Estimation via Capturing Head-face-eye Spatial-temporal Interaction Context](https://mlsanity.com/paper/2310.18131/) (2023)
- [What Do You See in Vehicle? Comprehensive Vision Solution for In-Vehicle Gaze Estimation](https://mlsanity.com/paper/2403.15664/) (2024)
- [Learning-by-Novel-View-Synthesis for Full-Face Appearance-Based 3D Gaze Estimation](https://mlsanity.com/paper/2201.07927/) (2022)
- [An End-to-End Review of Gaze Estimation and its Interactive Applications on Handheld Mobile Devices](https://mlsanity.com/paper/2307.00122/) (2023)
- [GazeNeRF: 3D-Aware Gaze Redirection with Neural Radiance Fields](https://mlsanity.com/paper/2212.04823/) (2022)
- [Spatio-Temporal Attention and Gaussian Processes for Personalized Video Gaze Estimation](https://mlsanity.com/paper/2404.05215/) (2024)
- [3DGazeNet: Generalizing Gaze Estimation with Weak-Supervision from Synthetic Views](https://mlsanity.com/paper/2212.02997/) (2022)
- [EFE: End-to-end Frame-to-Gaze Estimation](https://mlsanity.com/paper/2305.05526/) (2023)
- [MTGLS: Multi-Task Gaze Estimation with Limited Supervision](https://mlsanity.com/paper/2110.12100/) (2021)
- [NeRF-Gaze: A Head-Eye Redirection Parametric Model for Gaze Estimation](https://mlsanity.com/paper/2212.14710/) (2022)
- [Rotation-Constrained Cross-View Feature Fusion for Multi-View Appearance-based Gaze Estimation](https://mlsanity.com/paper/2305.12704/) (2023)
- [GazeGaussian: High-Fidelity Gaze Redirection with 3D Gaussian Splatting](https://mlsanity.com/paper/2411.12981/) (2024)
- [Enhancing 3D Gaze Estimation in the Wild using Weak Supervision with Gaze Following Labels](https://mlsanity.com/paper/2502.20249/) (2025)
- [LatentGaze: Cross-Domain Gaze Estimation through Gaze-Aware Analytic Latent Code Manipulation](https://mlsanity.com/paper/2209.10171/) (2022)
- [Test-Time Personalization with Meta Prompt for Gaze Estimation](https://mlsanity.com/paper/2401.01577/) (2024)
- [UniGaze: Towards Universal Gaze Estimation via Large-scale Pre-Training](https://mlsanity.com/paper/2502.02307/) (2025)
- [CrossGaze: A Strong Method for 3D Gaze Estimation in the Wild](https://mlsanity.com/paper/2402.08316/) (2024)
- [Onfocus Detection: Identifying Individual-Camera Eye Contact from Unconstrained Images](https://mlsanity.com/paper/2103.15307/) (2021)

## Abstract (arXiv)

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.