Abstract
Event-based eye tracking holds significant promise for fine-grained cognitive state inference, offering high temporal resolution and robustness to motion artifacts, critical features for decoding subtle mental states such as attention, confusion, or fatigue. In this work, we introduce a model-agnostic, inference-time refinement framework designed to enhance the output of existing event-based gaze estimation models without modifying their architecture or requiring retraining. Our method comprises two key post-processing modules: (i) Motion-Aware Median Filtering, which suppresses blink-induced spikes while preserving natural gaze dynamics, and (ii) Optical Flow-Based Local Refinement, which aligns gaze predictions with cumulative event motion to reduce spatial jitter and temporal discontinuities. To complement traditional spatial accuracy metrics, we propose a novel Jitter Metric that captures the temporal smoothness of predicted gaze trajectories based on velocity regularity and local signal complexity. Together, these contributions significantly improve the consistency of event-based gaze signals, making them better suited for downstream tasks such as micro-expression analysis and mind-state decoding. Our results demonstrate consistent improvements across multiple baseline models on controlled datasets, laying the groundwork for future integration with multimodal affect recognition systems in real-world environments.
Abstract (translated)
基于事件的眼动追踪在细粒度认知状态推断方面具有巨大的潜力，它提供了高时间分辨率和对运动伪影的鲁棒性，这是解码注意力、困惑或疲劳等微妙心理状态的关键特性。在这项工作中，我们提出了一种模型无关的推理时增强框架，旨在通过不修改现有基于事件的眼动估计模型架构且无需重新训练的方式来提升这些模型的输出效果。我们的方法包括两个关键后处理模块：(i) 运动感知中值滤波器，它抑制眨眼引起的尖峰信号，同时保持自然眼动动态；(ii) 基于光流的地方精化，将眼动预测与累积事件运动对齐，以减少空间抖动和时间不连续性。为了补充传统的空间准确性度量标准，我们提出了一种新颖的抖动度量指标，该指标基于速度规律性和局部信号复杂度捕捉预测的眼动轨迹的时间平滑度。这些贡献共同提高了基于事件眼动信号的一致性，使其更适合微表情分析和心理状态解码等下游任务。我们的结果在多个基准模型上显示出一致的改进效果，并且是在控制数据集上的成果，为未来与多模式情感识别系统在现实环境中的集成奠定了基础。
URL
https://arxiv.org/abs/2506.12524
