Non-intrusive, real-time analysis of the dynamics of the eye region allows us to monitor humans’ visual attention allocation and estimate their mental state during the performance of real-world tasks, which can potentially benefit a wide range of human-computer interaction (HCI) applications. While commercial eye-tracking devices have been frequently employed, the difficulty of customizing these devices places unnecessary constraints on the exploration of more efficient, end-to-end models of eye dynamics. In this work, we propose CLERA, a unified model for Cognitive Load and Eye Region Analysis, which achieves precise keypoint detection and spatiotemporal tracking in a joint-learning framework. Our method demonstrates significant efficiency and outperforms prior work on tasks including cognitive load estimation, eye landmark detection, and blink estimation. We also introduce a large-scale dataset of 30 k human faces with joint pupil, eye-openness, and landmark annotation, which aims at supporting future HCI research on human factors and eye-related analysis.

对眼部区域动态的非侵入式实时分析使我们能够监测人类的视觉注意力分配并估计他们在执行现实世界任务期间的心理状态，这可能有利于广泛的人机交互（人机交互）应用程序。虽然商业眼动追踪设备已被频繁使用，但定制这些设备的困难对探索更高效、端到端的眼动动力学模型造成了不必要的限制。在这项工作中，我们提出了 CLERA，一种用于认知负荷和眼区域分析的统一模型，它在联合学习框架中实现了精确的关键点检测和时空跟踪。我们的方法在认知负荷估计、眼部标志检测、和眨眼估计。我们还引入了包含联合瞳孔、睁眼度和地标标注的 3 万张人脸的大规模数据集，旨在支持未来关于人为因素和眼睛相关分析的人机交互研究。