发表机构
Chongqing Technology and Business University; School of Engineering, Westlake University; SAMOVAR, Telecom SudParis, Institute Polytechnique de Paris; Guangzhou College of Applied Science and Technology（重庆理工大学; 西湖大学工程学院; SAMOVAR，Telecom SudParis，巴黎高等理工学院; 广州应用科学与技术学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
提出AGVBench基准，评估30种数据增强策略在5个公开手掌/手指静脉数据集上的表现，发现多图像混合方法性能最优但校准差且易受对抗攻击，几何变换常降低识别效果，证明仅以准确率评估不足。
AI 中文摘要
静脉识别是一种安全的生物识别技术，常受限于有限的标注数据和成像变化。虽然数据增强可以缓解这一问题，但为自然图像设计的策略可能会破坏身份辨别所需的细粒度拓扑和纹理。我们提出AGVBench，在五个公开的手掌和手指静脉数据集上，使用七种骨干架构（包括经典CNN、视觉Transformer和静脉专用识别模型）评估了30种代表性增强策略。结果表明，多图像混合方法（如MixUp、PuzzleMix、StarMixup）通常提供最强的识别性能。然而，它们往往校准不良且易受对抗扰动影响，揭示了干净准确率与对抗安全性之间的明显不一致。我们还发现，严重的几何变换经常降低识别性能，这可能是由于特征错位或空间裁剪，并且增强效果在手掌和手指静脉数据集之间存在差异。这些发现证明，以准确率为中心的评估对于生物特征增强是不够的。AGVBench提供了标准化协议，以支持可重复研究并指导可靠、安全且鲁棒的静脉识别系统的设计。我们的代码库可在以下网址获取：https://this URL。
英文摘要
Vein recognition is a secure biometric technology often constrained by limited annotated data and imaging variations. While data augmentation mitigates this, strategies designed for natural images may disrupt the fine-grained topology and textures essential for identity discrimination. We present AGVBench, which evaluates 30 representative augmentation strategies on five public palm- and finger-vein datasets with seven backbone architectures, covering classic CNNs, vision transformers, and vein-specific recognition models. Our results show that multi-image mixing methods (e.g., MixUp, PuzzleMix, StarMixup) generally provide the strongest recognition performance. However, they are often poorly calibrated and vulnerable to adversarial perturbations, revealing a clear inconsistency between clean accuracy and adversarial security. We also find that severe geometric transformations frequently degrade recognition, which is potentially due to feature misalignment or spatial cropping, and that augmentation effectiveness varies across palm and finger vein datasets. These findings prove that accuracy-centric evaluation is insufficient for biometric augmentation. AGVBench provides standardized protocols to support reproducible research and guide the design of reliable, secure, and robust vein recognition systems. Our codebase is available at https://github.com/Advance-VeinTech-Innovators/AGVBench.
CommentsPreprint V1. Codebase: https://github.com/Advance-VeinTech-Innovators/AGVBench
