发表机构
Graduate School of Information Sciences, Tohoku University; Hitachi, Ltd.（东北大学信息科学研究生院; 日立有限公司）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对手指静脉图像年龄估计难题，提出MAGE-Vein多实例多任务学习框架，通过混合特征级融合提取衰老特征并抑制噪声，结合性别分类优化消除血管差异，在平衡数据集上取得良好效果，推翻传统认知。
AI 中文摘要
由于公共数据集中存在严重的人口统计学偏差以及性别等生理混杂因素，从手指静脉图像进行年龄估计一直被广泛认为不切实际。为克服这些限制，我们提出了MAGE-Vein，一种新颖的多实例、多任务学习框架。我们的方法通过采用三根手指的混合特征级融合来提取强大的结构衰老特征，有效抑制局部成像噪声。此外，性别分类的同步优化使网络能够有效消除特定性别的血管差异。在一个由402名受试者组成的人口统计学平衡数据集上进行评估，MAGE-Vein的平均绝对误差为6.12岁，相关性为0.880。我们的结果不仅推翻了关于手指静脉模态局限性的传统共识，还表明以前的估计失败主要是有偏差的公共数据集造成的假象。我们的代码可在这个https网址获取。
英文摘要
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.
Commentsaccepted to IJCB2026
