发表机构
Texas State University（德克萨斯州立大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对眼动生物识别的数据稀缺问题，提出多条件扩散模型EyeMakeYou，可生成符合身份、任务和主观状态约束的高频注视序列，在GazeBase上的实验表现优于现有方法，能有效扩充注视数据集。
AI 中文摘要
眼动生物识别（EMB）是一种新兴的用于用户身份验证的行为模态，尤其适用于虚拟现实和增强现实系统，其中注视动态包含独特的个体特定特征。然而，稳健的EMB系统需要多样化、高质量的注视记录，这类记录采集成本高昂，且往往无法满足模型开发所需的规模。生成模型可缓解数据稀缺问题，但现有方法要么合成通用的注视行为，要么主要通过身份来个性化信号，未同时表征用户的任务和主观状态。因此，生成的信号可能在视觉上看似真实，却无法保留生物识别应用所需的行为特性。为解决这一局限，我们提出EyeMakeYou，一种用于个体特定高频注视合成的多条件去噪扩散框架。EyeMakeYou从一条去除身份信息的参考轨迹生成5秒长、1000赫兹的双变量注视速度序列，并将去噪过程以身份嵌入、任务嵌入以及自我报告的整体难度、精神疲劳和眼部疲劳评分作为条件。其目标函数结合了扩散噪声预测与身份保留，以及多分辨率频谱、漂移一致性和事件加权局部平滑损失。在GazeBase数据集上的实验表明，EyeMakeYou相较于现有生成方法，在嵌入特征空间中实现了更高的中位空间精度和更强的真实-合成相似度，同时保留了主观报告与动眼特征之间选定的任务相关关联。这些发现支持条件扩散作为一种实用方法，可用于扩充生物识别和交互应用的注视数据集。
英文摘要
Eye movement biometrics (EMB) is an emerging behavioral modality for user authentication, particularly in virtual- and augmented-reality systems, where gaze dynamics contain distinctive subject-specific features. However, robust EMB systems require diverse, high-quality gaze recordings that are expensive to collect and often unavailable at the scale needed for model development. Generative models can mitigate data scarcity, but existing methods either synthesize generic gaze behavior or personalize signals primarily by identity, without jointly representing the user's task and subjective state. Consequently, generated signals may appear visually realistic while failing to retain the behavioral properties required for biometric applications. To address this limitation, we propose EyeMakeYou, a multi-conditional denoising diffusion framework for subject-specific, high-frequency gaze synthesis. EyeMakeYou generates 5-s, 1000-Hz bivariate gaze-velocity sequences from an identity-removed reference trajectory and conditions the denoising process on an identity embedding, a task embedding, and self-reported ratings of overall difficulty, mental tiredness, and eye tiredness. Its objective combines diffusion noise prediction and identity preservation with multi-resolution spectral, drift-consistency, and event-weighted local-smoothness losses. Experiments on GazeBase show that EyeMakeYou achieves higher median spatial accuracy and greater real--synthetic similarity in the embedding feature space than the existing generative approaches, while retaining selected task-dependent associations between subjective reports and oculomotor features. These findings support conditional diffusion as a practical approach for augmenting gaze datasets for biometric and interactive applications.
Comments20 pages, 3 tables, 3 figures
