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AGVBench: Reliability Benchmark Reveals Accuracy-Security Gap in Vein RecognitionAGVBench：静脉识别新基准揭示准确性与安全性矛盾
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R7-research
- Researchers open-sourced AGVBench, the first reliability-oriented benchmark for evaluating data augmentation strategies in palm- and finger-vein recognition systems.
- Systematic evaluation of 30 augmentation strategies across 5 datasets and 7 backbone architectures reveals multi-image mixing methods (MixUp, PuzzleMix) achieve highest accuracy but are poorly calibrated and vulnerable to adversarial attacks.
- Geometric transformations degrade recognition performance due to feature misalignment, and augmentation effectiveness varies significantly between palm-vein and finger-vein modalities, indicating no single strategy universally generalizes.
- 研究人员开源了 AGVBench，这是首个针对掌纹和指静脉识别数据增强的可靠性导向基准测试工具。
- 对 5 个数据集和 7 种主干网络上的 30 种增强策略的系统评估显示，多图混合方法（MixUp、PuzzleMix）准确度最高但校准不足，易受对抗攻击。
- 几何变换会因特征错位而降低识别性能，且增强策略在掌纹和指静脉识别间的有效性差异大，表明不存在通用的增强策略。
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AGVBench: A Reliability-Oriented Benchmark of Data Augmentation for Vein Recognition
2026-07-01
hf-daily-papers