# Towards High Performance Low Complexity Calibration in Appearance Based Gaze Estimation

arXiv: https://arxiv.org/abs/2001.09284 · HTML: https://mlsanity.com/paper/2001.09284/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Jan 2020.

## Built on by (most cited first, 6 of 6)

- [Appearance-based Gaze Estimation With Deep Learning: A Review and Benchmark](https://mlsanity.com/paper/2104.12668/) (2021)
- [MAC-Gaze: Motion-Aware Continual Calibration for Mobile Gaze Tracking](https://mlsanity.com/paper/2505.22769/) (2025)
- [EraW-Net: Enhance-Refine-Align W-Net for Scene-Associated Driver Attention Estimation](https://mlsanity.com/paper/2408.08570/) (2024)
- [Upper-Body Pose-based Gaze Estimation for Privacy-Preserving 3D Gaze Target Detection](https://mlsanity.com/paper/2409.17886/) (2024)
- [Investigating Bias and Fairness in Appearance-based Gaze Estimation](https://mlsanity.com/paper/2604.10707/) (2026)
- [Pose-Robust Calibration Strategy for Point-of-Gaze Estimation on Mobile Phones](https://mlsanity.com/paper/2508.10268/) (2025)

## Abstract (arXiv)

Appearance-based gaze estimation from RGB images provides relatively unconstrained gaze tracking. We have previously proposed a gaze decomposition method that decomposes the gaze angle into the sum of a subject-independent gaze estimate from the image and a subject-dependent bias. This paper extends that work with a more complete characterization of the interplay between the complexity of the calibration dataset and estimation accuracy. We analyze the effect of the number of gaze targets, the number of images used per gaze target and the number of head positions in calibration data using a new NISLGaze dataset, which is well suited for analyzing these effects as it includes more diversity in head positions and orientations for each subject than other datasets. A better understanding of these factors enables low complexity high performance calibration. Our results indicate that using only a single gaze target and single head position is sufficient to achieve high quality calibration, outperforming state-of-the-art methods by more than 6.3%. One of the surprising findings is that the same estimator yields the best performance both with and without calibration. To better understand the reasons, we provide a new theoretical analysis that specifies the conditions under which this can be expected.