Abstract
We introduce EyeTheia, a lightweight and open deep learning pipeline for webcam-based gaze estimation, designed for browser-based experimental platforms and real-world cognitive and clinical research. EyeTheia enables real-time gaze tracking using only a standard laptop webcam, combining MediaPipe-based landmark extraction with a convolutional neural network inspired by iTracker and optional user-specific fine-tuning. We investigate two complementary strategies: adapting a model pretrained on mobile data and training the same architecture from scratch on a desktop-oriented dataset. Validation results on MPIIFaceGaze show comparable performance between both approaches prior to calibration, while lightweight user-specific fine-tuning consistently reduces gaze prediction error. We further evaluate EyeTheia in a realistic Dot-Probe task and compare it to the commercial webcam-based tracker SeeSo SDK. Results indicate strong agreement in left-right gaze allocation during stimulus presentation, despite higher temporal variability. Overall, EyeTheia provides a transparent and extensible solution for low-cost gaze tracking, suitable for scalable and reproducible experimental and clinical studies. The code, trained models, and experimental materials are publicly available.
Abstract (translated)
我们介绍EyeTheia，这是一个轻量级的开源深度学习流水线，用于基于网络摄像头的眼动追踪估计，旨在为浏览器基础实验平台和现实世界中的认知及临床研究设计。EyeTheia能够仅使用标准笔记本电脑内置网络摄像头实现实时眼动追踪，它结合了MediaPipe基元的特征点提取与受iTracker启发的卷积神经网络，并提供可选的用户特定微调选项。 我们调查了两种互补策略：一种是在移动数据上预训练模型进行适应，另一种是从头开始在以桌面为中心的数据集上训练相同架构。在MPIIFaceGaze验证结果表明，在校准之前，这两种方法表现出相当的性能，而轻量级的用户特定微调选项能持续减少眼动预测误差。 我们进一步在一个现实的Dot-Probe任务中评估EyeTheia，并将其与商用网络摄像头眼动追踪器SeeSo SDK进行比较。结果显示，在呈现刺激时，左右眼动分配有强烈的符合性，尽管时间变异性较高。总体而言，EyeTheia为低成本眼动追踪提供了一个透明且可扩展的解决方案，适合大规模和可重复性的实验及临床研究。 该项目的代码、训练好的模型以及实验材料均公开可用。
URL
https://arxiv.org/abs/2601.06279
