构建跨谱面通过车窗人脸数据集，助力移动边境安检人脸识别
DriveFace: A Cross-Spectral Through-Glass Face Dataset for On-the-Move Vehicular Border Control
- 采集车辆行驶中红外视频与手机预注册数据，模拟真实边境检查场景
- 现有模型在动态模糊、光照变化下识别率显著下降，性能受限明显
- 为交通场景下的跨谱面人脸识别提供基准数据，适合安防与智能交通研究
跨境流动持续增长给现有边境管控设施带来压力，推动在移动中进行生物特征认证的发展——即旅客在车辆内直接完成身份识别。人脸具有被动、远距离获取的优势，适合该场景，但其发展受限于缺乏代表性数据集：现有基准多在受控环境下采集，未能体现车辆采集中的挑战，如运动模糊、光照变化、遮挡及跨谱段注册问题。为此，本文提出一个面向移动式边境控制的人脸识别数据集，包含红外（NIR）车辆通行视频与智能手机预注册数据。基于主流模型的基线评估表明，在真实复杂条件下性能明显下降，凸显开发专用方法的必要性。
原文摘要 · Abstract (English)
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.
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