MAGE-Vein: Multi-Instance Age and Gender Estimation from Finger Vein Images

2026-07-22

AI Summary

Finger vein imaging has long been considered unsuitable for age estimation, primarily due to demographic biases in publicly available datasets and confounding physiological factors such as gender. This work proposes a multi-instance, multi-task learning framework that performs feature-level fusion across three fingers to extract structured aging-related features, while jointly optimizing gender classification to mitigate gender-specific vascular variations. For the first time, the study demonstrates high-accuracy age estimation from finger vein images on a balanced dataset of 402 subjects, achieving a mean absolute error of 6.12 years and a correlation coefficient of 0.880. These results challenge the prevailing consensus regarding the modality’s inadequacy, revealing that prior failures stemmed from data bias rather than inherent limitations of finger vein imaging itself.

Abstract

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.

Problem

• age estimation
• finger vein images
• demographic bias
• gender confounding
• biometric modality

Innovation

• multi-instance learning
• multi-task learning
• finger vein biometrics
• age estimation
• gender classification