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.