融合眼动与眼周图像，实现无需校准的高精度身份认证
Ocular Authentication: Fusion of Gaze and Periocular Modalities
- 构建统一眼动与眼周图像的多模态认证系统
- 在9202人数据集上超越单一模态和FIDO基准
- 适合无校准、高安全场景的身份验证应用
本文研究了在无需校准的身份认证系统中，融合眼动与眼周图像两种眼区生物特征的可行性。尽管每种模态独立已显示出认证潜力，但二者在统一的眼动估计流程中大规模结合尚未充分探索。本研究提出一种多模态认证系统，并基于包含9202名受试者的大型自建数据集进行评估，其眼动信号质量相当于消费级虚拟现实（VR）设备水平。结果表明，多模态方法在所有场景下均持续优于单一模态系统，且超越FIDO基准。采用先进的机器学习架构显著提升了整体认证性能，得益于模型对认证特征的捕捉能力以及两种模态间互补的判别特性。
原文摘要 · Abstract (English)
This paper investigates the feasibility of fusing two eye-centric authentication modalities-eye movements and periocular images-within a calibration-free authentication system. While each modality has independently shown promise for user authentication, their combination within a unified gaze-estimation pipeline has not been thoroughly explored at scale. In this report, we propose a multimodal authentication system and evaluate it using a large-scale in-house dataset comprising 9202 subjects with an eye tracking (ET) signal quality equivalent to a consumer-facing virtual reality (VR) device. Our results show that the multimodal approach consistently outperforms both unimodal systems across all scenarios, surpassing the FIDO benchmark. The integration of a state-of-the-art machine learning architecture contributed significantly to the overall authentication performance at scale, driven by the model's ability to capture authentication representations and the complementary discriminative characteristics of the fused modalities.
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