AGVBench: A Reliability-Oriented Benchmark of Data Augmentation for Vein Recognition
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, PuzzleMi
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- LinkedLinked via arxiv author · 85%Haiyang Li →
“AGVBench: A Reliability-Oriented Benchmark of Data Augmentation for Vein Recognition”
- LinkedLinked via arxiv author · 85%Yuming Fu →
“AGVBench: A Reliability-Oriented Benchmark of Data Augmentation for Vein Recognition”
- LinkedLinked via arxiv author · 85%Qun Song →
“AGVBench: A Reliability-Oriented Benchmark of Data Augmentation for Vein Recognition”
- LinkedLinked via arxiv author · 85%Hongchao Liao →
“AGVBench: A Reliability-Oriented Benchmark of Data Augmentation for Vein Recognition”
- LinkedLinked via arxiv author · 85%Jing Chen →
“AGVBench: A Reliability-Oriented Benchmark of Data Augmentation for Vein Recognition”
- LinkedLinked via arxiv author · 85%Mounim A. EI-Yacoubi →
“AGVBench: A Reliability-Oriented Benchmark of Data Augmentation for Vein Recognition”
- LinkedLinked via arxiv author · 85%Xin Jin →
“AGVBench: A Reliability-Oriented Benchmark of Data Augmentation for Vein Recognition”