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This study introduces AGVBench, a benchmark designed to evaluate the effectiveness of 30 data augmentation strategies for vein recognition across five datasets and various backbone architectures. The research reveals that while multi-image mixing methods like MixUp and PuzzleMix enhance recognition performance, they exhibit poor calibration and vulnerability to adversarial attacks, highlighting a critical gap between clean accuracy and adversarial robustness. Additionally, the findings indicate that excessive geometric transformations can hinder recognition accuracy, emphasizing the need for a more nuanced approach to biometric data augmentation beyond mere accuracy metrics.
Multi-image mixing methods boost vein recognition performance but compromise adversarial security, revealing a critical trade-off in biometric systems.
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.
