  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 30k human faces with joint pupil, eye-openness, and landmark
annotation, which aims to support future HCI research on human factors and
eye-related analysis.
