mlsanityData to 7 Oct 2026

AGVBench: A Reliability-Oriented Benchmark of Data Augmentation for Vein Recognition

arXiv 2607.02271 ↗First posted 2 Jul 2026cs.CVcited by 0

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Abstract (arXiv)

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.

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References · 37

  1. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  2. 2020An Image is Worth 16x16 Words: Transformers for Image Recognition at Scalecited by 29,915
  3. 2021Swin Transformer: Hierarchical Vision Transformer using Shifted Windowscited by 12,082
  4. 2014Explaining and Harnessing Adversarial Examplescited by 7,287
  5. 2017Towards Deep Learning Models Resistant to Adversarial Attackscited by 6,326
  6. 2015Rethinking the Inception Architecture for Computer Visioncited by 5,451
  7. 2017mixup: Beyond Empirical Risk Minimizationcited by 5,155
  8. 2016SGDR: Stochastic Gradient Descent with Warm Restartscited by 4,993
  9. 2018MobileNetV2: Inverted Residuals and Linear Bottleneckscited by 4,773
  10. 2019CutMix: Regularization Strategy to Train Strong Classifiers with Localizable Featurescited by 2,875
  11. 2019Benchmarking Neural Network Robustness to Common Corruptions and Perturbationscited by 2,857
  12. 2019RandAugment: Practical automated data augmentation with a reduced search spacecited by 2,261
25 more
  1. 2017Improved Regularization of Convolutional Neural Networks with Cutoutcited by 1,623
  2. 2017Random Erasing Data Augmentationcited by 1,235
  3. 2019AugMix: A Simple Data Processing Method to Improve Robustness and Uncertaintycited by 897
  4. 2017Regularizing Neural Networks by Penalizing Confident Output Distributionscited by 461
  5. 2014Training Deep Neural Networks on Noisy Labels with Bootstrappingcited by 322
  6. 2020Puzzle Mix: Exploiting Saliency and Local Statistics for Optimal Mixupcited by 260
  7. 2021TrivialAugment: Tuning-free Yet State-of-the-Art Data Augmentationcited by 225
  8. 2020SaliencyMix: A Saliency Guided Data Augmentation Strategy for Better Regularizationcited by 149
  9. 2020GridMask Data Augmentationcited by 123
  10. 2020Delving Deep into Label Smoothingcited by 94
  11. 2018Data Augmentation using Random Image Cropping and Patching for Deep CNNscited by 87
  12. 2024DiffuseMix: Label-Preserving Data Augmentation with Diffusion Modelscited by 78
  13. 2021AutoMix: Unveiling the Power of Mixup for Stronger Classifierscited by 69
  14. 2020ResizeMix: Mixing Data with Preserved Object Information and True Labelscited by 68
  15. 2020KeepAugment: A Simple Information-Preserving Data Augmentation Approachcited by 52
  16. 2022TeachAugment: Data Augmentation Optimization Using Teacher Knowledgecited by 37
  17. 2023GuidedMixup: An Efficient Mixup Strategy Guided by Saliency Mapscited by 23
  18. 2022You Only Cut Once: Boosting Data Augmentation with a Single Cutcited by 22
  19. 2023Adversarial AutoMixupcited by 16
  20. 2024GenMix: Effective Data Augmentation with Generative Diffusion Model Image Editingcited by 14
  21. 2022Soft Augmentation for Image Classificationcited by 11
  22. 2025Context-guided Responsible Data Augmentation with Diffusion Modelscited by 9
  23. 2020Self-Progressing Robust Trainingcited by 8
  24. 2022Randomized Quantization: A Generic Augmentation for Data Agnostic Self-supervised Learningcited by 6
  25. 2026S2-FracMix: Label-Preserving Self-Saliency Mixup Augmentationcited by 2

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