AGVBench：面向静脉识别的数据增强可靠性基准

摘要

靜脈辨識是一項安全的生物特徵識別技術，常受限於標註資料稀少與成像變異。雖然資料增強有助於緩解此問題，但專為自然影像設計的策略可能破壞辨別身分所需的細粒度拓撲結構與紋理。我們提出AGVBench，針對五個公開手掌與手指靜脈資料集，搭配七種骨幹架構（涵蓋經典CNN、視覺Transformer及靜脈專用辨識模型），評估了30種具代表性的增強策略。結果顯示，多圖混合方法（如MixUp、PuzzleMix、StarMixup）普遍提供最強的辨識效能，然而這些方法往往校準不佳且易受對抗性擾動影響，反映出乾淨準確度與對抗安全性之間明顯的不一致性。我們也發現，劇烈的幾何變換經常導致辨識效能下降，可能源於特徵錯位或空間裁切；同時，增強效果在手掌握靜脈資料集之間存在差異。這些發現證明僅以準確度為中心的評量不足以衡量生物特徵增強。AGVBench提供標準化流程，支援可重現研究，並引導設計可靠、安全且穩健的靜脈辨識系統。我們的程式碼庫公開於 https://github.com/Advance-VeinTech-Innovators/AGVBench。

English

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.