# The Story in Your Eyes: An Individual-difference-aware Model for Cross-person Gaze Estimation

arXiv: https://arxiv.org/abs/2106.14183 · HTML: https://mlsanity.com/paper/2106.14183/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 35 references that are in the map.
- First posted on arXiv 27 Jun 2021.

## Builds on (most cited first, 14 of 14)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification](https://mlsanity.com/paper/1502.01852/) (2015)
- [Spatial Transformer Networks](https://mlsanity.com/paper/1506.02025/) (2015)
- [Eye Tracking for Everyone](https://mlsanity.com/paper/1606.05814/) (2016)
- [Appearance-Based Gaze Estimation in the Wild](https://mlsanity.com/paper/1504.02863/) (2015)
- [ETH-XGaze: A Large Scale Dataset for Gaze Estimation under Extreme Head Pose and Gaze Variation](https://mlsanity.com/paper/2007.15837/) (2020)
- [MPIIGaze: Real-World Dataset and Deep Appearance-Based Gaze Estimation](https://mlsanity.com/paper/1711.09017/) (2017)
- [Few-Shot Adaptive Gaze Estimation](https://mlsanity.com/paper/1905.01941/) (2019)
- [Connecting Gaze, Scene, and Attention: Generalized Attention Estimation via Joint Modeling of Gaze and Scene Saliency](https://mlsanity.com/paper/1807.10437/) (2018)
- [Deep Pictorial Gaze Estimation](https://mlsanity.com/paper/1807.10002/) (2018)
- [Improving Few-Shot User-Specific Gaze Adaptation via Gaze Redirection Synthesis](https://mlsanity.com/paper/1904.10638/) (2019)
- [Towards End-to-end Video-based Eye-Tracking](https://mlsanity.com/paper/2007.13120/) (2020)
- [Learning to Find Eye Region Landmarks for Remote Gaze Estimation in Unconstrained Settings](https://mlsanity.com/paper/1805.04771/) (2018)
- [A Differential Approach for Gaze Estimation](https://mlsanity.com/paper/1904.09459/) (2019)

## Built on by (most cited first, 5 of 5)

- [End-to-end Video Gaze Estimation via Capturing Head-face-eye Spatial-temporal Interaction Context](https://mlsanity.com/paper/2310.18131/) (2023)
- [An End-to-End Review of Gaze Estimation and its Interactive Applications on Handheld Mobile Devices](https://mlsanity.com/paper/2307.00122/) (2023)
- [Modeling State Shifting via Local-Global Distillation for Event-Frame Gaze Tracking](https://mlsanity.com/paper/2404.00548/) (2024)
- [Gaze-Vector Estimation in the Dark with Temporally Encoded Event-driven Neural Networks](https://mlsanity.com/paper/2403.02909/) (2024)
- [Learning Spatio-Temporal Feature Representations for Video-Based Gaze Estimation](https://mlsanity.com/paper/2512.17673/) (2025)

## Abstract (arXiv)

We propose a novel method on refining cross-person gaze prediction task with eye/face images only by explicitly modelling the person-specific differences. Specifically, we first assume that we can obtain some initial gaze prediction results with existing method, which we refer to as InitNet, and then introduce three modules, the Validity Module (VM), Self-Calibration (SC) and Person-specific Transform (PT)) Module. By predicting the reliability of current eye/face images, our VM is able to identify invalid samples, e.g. eye blinking images, and reduce their effects in our modelling process. Our SC and PT module then learn to compensate for the differences on valid samples only. The former models the translation offsets by bridging the gap between initial predictions and dataset-wise distribution. And the later learns more general person-specific transformation by incorporating the information from existing initial predictions of the same person. We validate our ideas on three publicly available datasets, EVE, XGaze and MPIIGaze and demonstrate that our proposed method outperforms the SOTA methods significantly on all of them, e.g. respectively 21.7%, 36.0% and 32.9% relative performance improvements. We won the GAZE 2021 Competition on the EVE dataset. Our code can be found here https://github.com/bjj9/EVE_SCPT.