Scholarlink

<p>Finger vein recognition faces two major challenges: the degradation of recognition performance due to varying lighting conditions during image acquisition and the limitation of available training data. This paper proposes FV-Res50ED, a novel model designed to address both issues. First, we develop a Focus-ROI method to isolate key finger vein regions, excluding irrelevant background areas. Second, we propose a Dynamic Contrast-Limited Adaptive (DCLA) enhancement method. It dynamically adjusts the contrast limit using local image features, efficiently reducing blur and illumination-induced artifacts. Third, we introduce a Triplet Angular Adaptive Margin (TAAM) loss, which integrates angular loss into triplet loss, utilizing cosine similarity to strengthen feature discrimination. Furthermore, TAAM adopts a adaptive learnable margin to enhance the discriminative power of features. Experimental results on three public datasets show that FV-Res50ED achieves recognition accuracies of 99.69%, 99.36%, and 99.52%, respectively, with an average accuracy improvement of 1.29% and a 43.67% reduction in equal error rate (EER) compared to existing methods.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

FV-Res50ED: a finger vein recognition model with dynamic contrast enhancement and triplet angular adaptive margin

  • 期刊论文

  • 发表日期：

    2026年3月30日

查看全文
  • Luokun Yang,
  • Yantao Zhang,
  • Jun Li,
  • Mingquan Ye,
  • Yang Su,
  • Juntong Liu,
  • Jie Chang

摘要

Finger vein recognition faces two major challenges: the degradation of recognition performance due to varying lighting conditions during image acquisition and the limitation of available training data. This paper proposes FV-Res50ED, a novel model designed to address both issues. First, we develop a Focus-ROI method to isolate key finger vein regions, excluding irrelevant background areas. Second, we propose a Dynamic Contrast-Limited Adaptive (DCLA) enhancement method. It dynamically adjusts the contrast limit using local image features, efficiently reducing blur and illumination-induced artifacts. Third, we introduce a Triplet Angular Adaptive Margin (TAAM) loss, which integrates angular loss into triplet loss, utilizing cosine similarity to strengthen feature discrimination. Furthermore, TAAM adopts a adaptive learnable margin to enhance the discriminative power of features. Experimental results on three public datasets show that FV-Res50ED achieves recognition accuracies of 99.69%, 99.36%, and 99.52%, respectively, with an average accuracy improvement of 1.29% and a 43.67% reduction in equal error rate (EER) compared to existing methods.