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.

翻译：我们提出EyeTheia，一种面向基于网络摄像头的视线估计的轻量级开源深度学习流水线，专为浏览器实验平台及现实场景中的认知与临床研究设计。EyeTheia仅凭标准笔记本电脑摄像头即可实现实时眼动追踪，融合基于MediaPipe的 landmarks提取、受iTracker启发的卷积神经网络，并支持可选的用户特定微调。我们探索两种互补策略：迁移基于移动端数据预训练的模型，以及在同一架构上从零训练桌面端数据集。在MPIIFaceGaze数据集上的验证结果表明，校准前两种方法的性能相当，而轻量级用户特定微调总能降低视线预测误差。我们进一步在真实感点探测任务中评估EyeTheia，并将其与商业网络摄像头追踪器SeeSo SDK进行比较。结果显示，尽管存在较高时间变异性，但刺激呈现期间左右视线分配呈现高度一致性。总体而言，EyeTheia为低成本眼动追踪提供了透明且可扩展的解决方案，适用于可规模化、可复现的实验与临床研究。相关代码、训练模型及实验材料均已公开。