发表机构
Idiap Research Institute; University of Lausanne (UNIL)（Idiap研究所; 洛桑大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对跨境流动性增长带来的边境管制压力，提出移动生物特征认证。因缺乏合适数据集阻碍人脸识别发展，引入DriveFace数据集，含近红外车辆过境视频与预注册数据，用先进模型评估发现性能受限，凸显需专门方法推动该领域进步。
AI 中文摘要
跨境流动性的持续增长给现有边境管制基础设施带来了越来越大的压力，促使进行移动生物特征认证，即在检查站直接在车内识别旅行者。人脸识别非常适合这种场景，因为它可以被动且远距离获取。然而，其发展受到缺乏代表性数据集的阻碍：现有的基准是在受控环境中收集的，没有捕捉到车辆采集所固有的挑战，包括运动模糊、可变光照、遮挡和跨光谱注册。为了解决这一差距，我们引入了一个用于边境管制场景中移动人脸识别的数据集，该数据集由近红外车辆过境视频与基于智能手机的预注册数据配对组成。使用最先进模型的基线评估显示在这些现实条件下存在明显的性能限制，突出了需要专门方法来推动该领域发展。
英文摘要
The continuous growth in cross-border mobility places increasing pressure on existing border control infrastructures, motivating on-the-move biometric authentication, in which travellers are identified directly inside their vehicles at checkpoints. Face recognition is well-suited to this setting, as it can be acquired passively and at a distance. Its development, however, is hindered by the lack of representative datasets: existing benchmarks are collected in controlled environments and do not capture the challenges inherent to vehicular acquisition, including motion blur, variable illumination, occlusions, and cross-spectral enrollment. To address this gap, we introduce a dataset for on-the-move face recognition in border-control scenarios, comprising NIR vehicle-crossing videos paired with smartphone-based pre-enrollment data. Baseline evaluations with state-of-the-art models show clear performance limitations under these realistic conditions, highlighting the need for dedicated methods to advance the field.
CommentsAccepted in IJCB 2026; Project page: https://www.idiap.ch/paper/driveface/
