挑战性条件下基于视频的掌纹认证

中文摘要 AI 辅助

掌纹生物特征越来越多地用于安全的非接触式认证。然而，实际部署中会面临表面噪声（汗水、污垢）、光照和运动变化，以及温度导致的血管可见性变化，由于缺乏此类条件下采集的数据，这些问题仍未得到充分研究。为研究这些影响，我们推出了哥伦比亚大学掌纹（CUP）数据集，据我们所知，这是首个公开的基于视频的掌纹数据集。CUP在四种表面条件（干净基线、温暖、潮湿、脏污）下记录每只手掌，并为每个主体配对生理和人口统计元数据。我们在该数据集上对21种识别器进行基准测试，涵盖静态、视频和多帧聚合架构。在干净手掌上表现可靠的模型在脏污手掌上会丧失大部分准确率，平均等错误率（EER）约增加三倍。我们在采集的两个维度上恢复了大部分鲁棒性：时间维度上，对传感器已返回的少量帧达成共识可消除瞬态损坏；空间维度上，一种无需添加学习参数的测试时匹配器将全局余弦相似度与显著性引导的区域级最优传输相融合，该传输会绕开损坏区域进行比对。完整设计在CUP的所有表面条件下，在EER、FAR=0.01时的真接受率（TAR）和排名1指标上均领先，参数量为430万，浮点运算量为31亿次，仅为视频模型成本的一小部分。将其附加到四个冻结的最先进骨干网络上，无需重新训练即可将它们的平均EER降低29%-37%，且在四个公开单图像数据集上，仅区域匹配仍有帮助。对10个人口统计和生理特征的初步审查发现，在温暖条件下存在两个与身体水分和性别相关的差距，且经多重比较校正后仍然存在。CUP将在发表后通过该httpsURL发布，供非商业研究使用。

英文摘要

Palm-vein biometrics are increasingly used for secure, contactless authentication. Yet real-world deployment exposes them to surface noise (sweat, dirt), illumination and motion variation, and temperature-driven changes in vascular visibility, which remain underexplored for lack of data captured under such conditions. To study these effects, we introduce the Columbia University Palm-vein (CUP) dataset, to our knowledge the first public video-based palm-vein dataset. CUP records every palm under four surface conditions (a clean baseline, warm, wet, and dirty) and pairs each subject with physiological and demographic metadata. On it we benchmark twenty-one recognizers spanning static, video, and multi-frame aggregation architectures. Models that verify reliably on clean palms lose most of their accuracy on dirty ones, and the mean equal error rate (EER) roughly quadruples. We recover much of that robustness along both axes of the capture. Temporally, a consensus over the few frames the sensor already returns cancels transient corruption; spatially, a test-time matcher that adds no learned parameters fuses the global cosine with a saliency-steered region-level optimal transport that routes the comparison around corrupted regions. The full design leads on every surface of CUP in EER, TAR@FAR=0.01, and Rank-1, at 4.3M parameters and 3.1 GFLOPs, a fraction of the video models' cost. Attached to four frozen state-of-the-art backbones it cuts their mean EER by 29-37% without retraining, and on four public single-image datasets the regional matching alone still helps. A preliminary audit across ten demographic and physiological traits finds two warm-condition gaps, along body water and gender, that survive multiple-comparison correction. CUP will be released for non-commercial research use at https://github.com/MobileX-CU/CUP_v1 upon publication.

发表机构

• Columbia University（哥伦比亚大学）
• Princeton University（普林斯顿大学）
• Stanford University（斯坦福大学）
• Amazon.com Services LLC（亚马逊服务有限责任公司）

机构由 AI 辅助整理，请以论文原文为准。