用多指融合与联合性别估计，提升指纹静脉年龄预测精度
MAGE-Vein: Multi-Instance Age and Gender Estimation from Finger Vein Images
- 多指特征融合+联合性别任务，抑制噪声与性别偏差
- 在402人平衡数据集上误差仅6.12年，相关性达0.880
- 揭示以往失败源于数据偏差，为活体识别提供新思路
由于公开数据集存在严重人口统计学偏差及性别等生理因素干扰，指纹静脉图像的年龄估计长期被视为不切实际。为克服这一局限，我们提出MAGE-Vein——一种新型多实例、多任务学习框架。通过融合三根手指的特征，有效提取稳健的结构化衰老标志，并抑制局部成像噪声；同时联合优化性别分类任务，使网络能够消除性别相关的血管差异。在包含402名受试者的均衡数据集上，MAGE-Vein实现均方误差6.12年，相关系数0.880。结果不仅推翻了指纹静脉模态不可行的传统共识，还表明此前的估计失败主要源于数据偏差。代码已开源。
原文摘要 · Abstract (English)
Age estimation from finger vein images has been widely considered impractical due to severe demographic biases in public datasets and physiological confounding factors like gender. To overcome these limitations, we propose MAGE-Vein, a novel multi-instance, multi-task learning framework. Our approach extracts robust structural aging signs by employing a hybrid feature-level fusion of three fingers, effectively suppressing local imaging noise. Furthermore, simultaneous optimization of gender classification conditions the network to effectively eliminate gender-specific vascular variations. Evaluated on a demographically balanced dataset of 402 subjects, MAGE-Vein achieves a mean absolute error of 6.12 years and a correlation of 0.880. Our results not only overturn the conventional consensus regarding the limitations of the finger vein modality but also demonstrate that previous estimation failures were primarily artifacts of biased public datasets. Our code is available at https://github.com/gsisaoki/MAGE-Vein.
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