# Light-weight Head Pose Invariant Gaze Tracking

arXiv: https://arxiv.org/abs/1804.08572 · HTML: https://mlsanity.com/paper/1804.08572/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Apr 2018.

## Built on by (most cited first, 14 of 14)

- [Gaze Estimation using Transformer](https://mlsanity.com/paper/2105.14424/) (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)
- [Synthetic Data in Human Analysis: A Survey](https://mlsanity.com/paper/2208.09191/) (2022)
- [Eye-tracked Virtual Reality: A Comprehensive Survey on Methods and Privacy Challenges](https://mlsanity.com/paper/2305.14080/) (2023)
- [FLAME: Facial Landmark Heatmap Activated Multimodal Gaze Estimation](https://mlsanity.com/paper/2110.04828/) (2021)
- [Modeling State Shifting via Local-Global Distillation for Event-Frame Gaze Tracking](https://mlsanity.com/paper/2404.00548/) (2024)
- [Analysis of Classifier Training on Synthetic Data for Cross-Domain Datasets](https://mlsanity.com/paper/2410.22748/) (2024)
- [OmniGaze: Reward-inspired Generalizable Gaze Estimation In The Wild](https://mlsanity.com/paper/2510.13660/) (2025)
- [Automated Visual Attention Detection using Mobile Eye Tracking in Behavioral Classroom Studies](https://mlsanity.com/paper/2505.07552/) (2025)
- [Dataset for eye-tracking tasks](https://mlsanity.com/paper/2106.07554/) (2021)
- [Cycle-GAN for eye-tracking](https://mlsanity.com/paper/2205.10556/) (2022)
- [GADS: A Super Lightweight Model for Head Pose Estimation](https://mlsanity.com/paper/2504.15751/) (2025)
- [Pose-Robust Calibration Strategy for Point-of-Gaze Estimation on Mobile Phones](https://mlsanity.com/paper/2508.10268/) (2025)

## Abstract (arXiv)

Unconstrained remote gaze tracking using off-the-shelf cameras is a challenging problem. Recently, promising algorithms for appearance-based gaze estimation using convolutional neural networks (CNN) have been proposed. Improving their robustness to various confounding factors including variable head pose, subject identity, illumination and image quality remain open problems. In this work, we study the effect of variable head pose on machine learning regressors trained to estimate gaze direction. We propose a novel branched CNN architecture that improves the robustness of gaze classifiers to variable head pose, without increasing computational cost. We also present various procedures to effectively train our gaze network including transfer learning from the more closely related task of object viewpoint estimation and from a large high-fidelity synthetic gaze dataset, which enable our ten times faster gaze network to achieve competitive accuracy to its current state-of-the-art direct competitor.