# Offset Calibration for Appearance-Based Gaze Estimation via Gaze Decomposition

arXiv: https://arxiv.org/abs/1905.04451 · HTML: https://mlsanity.com/paper/1905.04451/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 May 2019.

## Built on by (most cited first, 13 of 13)

- [Appearance-based Gaze Estimation With Deep Learning: A Review and Benchmark](https://mlsanity.com/paper/2104.12668/) (2021)
- [Automatic Gaze Analysis: A Survey of Deep Learning based Approaches](https://mlsanity.com/paper/2108.05479/) (2021)
- [Weakly-Supervised Physically Unconstrained Gaze Estimation](https://mlsanity.com/paper/2105.09803/) (2021)
- [Contrastive Representation Learning for Gaze Estimation](https://mlsanity.com/paper/2210.13404/) (2022)
- [Spatio-Temporal Attention and Gaussian Processes for Personalized Video Gaze Estimation](https://mlsanity.com/paper/2404.05215/) (2024)
- [Test-Time Personalization with Meta Prompt for Gaze Estimation](https://mlsanity.com/paper/2401.01577/) (2024)
- [Towards Self-Supervised Gaze Estimation](https://mlsanity.com/paper/2203.10974/) (2022)
- [MAC-Gaze: Motion-Aware Continual Calibration for Mobile Gaze Tracking](https://mlsanity.com/paper/2505.22769/) (2025)
- [GazeGen: Gaze-Driven User Interaction for Visual Content Generation](https://mlsanity.com/paper/2411.04335/) (2024)
- [Alfa: Attentive Low-Rank Filter Adaptation for Structure-Aware Cross-Domain Personalized Gaze Estimation](https://mlsanity.com/paper/2603.08445/) (2026)
- [Investigating Bias and Fairness in Appearance-based Gaze Estimation](https://mlsanity.com/paper/2604.10707/) (2026)
- [Detection of Abnormal Behavior with Self-Supervised Gaze Estimation](https://mlsanity.com/paper/2107.06530/) (2021)
- [Self-Paced Deep Regression Forests with Consideration of Ranking Fairness](https://mlsanity.com/paper/2112.06455/) (2021)

## Abstract (arXiv)

Appearance-based gaze estimation provides relatively unconstrained gaze tracking. However, subject-independent models achieve limited accuracy partly due to individual variations. To improve estimation, we propose a novel gaze decomposition method and a single gaze point calibration method, motivated by our finding that the inter-subject squared bias exceeds the intra-subject variance for a subject-independent estimator. We decompose the gaze angle into a subject-dependent bias term and a subject-independent term between the gaze angle and the bias. The subject-independent term is estimated by a deep convolutional network. For calibration-free tracking, we set the subject-dependent bias term to zero. For single gaze point calibration, we estimate the bias from a few images taken as the subject gazes at a point. Experiments on three datasets indicate that as a calibration-free estimator, the proposed method outperforms the state-of-the-art methods by up to 10.0%. The proposed calibration method is robust and reduces estimation error significantly (up to 35.6%), achieving state-of-the-art performance for appearance-based eye trackers with calibration.