AGVBench：面向可靠性的静脉识别数据增强基准

摘要

静脉识别作为一种安全的生物特征识别技术，常受限于标注数据不足和成像变异问题。虽然数据增强可缓解此类问题，但针对自然图像设计的增强策略可能破坏身份识别所需的细粒度拓扑结构与纹理特征。我们提出AGVBench基准测试平台，在五个公开掌静脉与指静脉数据集上，结合七种骨干网络架构（涵盖经典卷积神经网络、视觉Transformer及静脉专用识别模型），系统评估了30种代表性数据增强策略。实验结果表明，多图像混合方法（如MixUp、PuzzleMix、StarMixup）通常能提供最强的识别性能。然而，这类方法往往存在校准偏差且易受对抗性扰动影响，揭示出干净准确率与对抗安全性之间的显著不一致性。研究还发现，剧烈的几何变换常导致识别性能下降，可能源于特征错位或空间裁剪；同时增强效果在掌静脉与指静脉数据集中存在差异。这些发现证明，仅以准确率为中心的评估不足以指导生物特征数据增强。AGVBench提供标准化实验协议，以支持可复现研究并引导设计可靠、安全且鲁棒的静脉识别系统。我们的代码库已开源：https://github.com/Advance-VeinTech-Innovators/AGVBench。

English

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.