Deep Representation-Based Feature Extraction and Recovering for Finger-Vein Verification
Deep Representation-Based Feature Extraction and Recovering for Finger-Vein Verification
复制标题
基于深度表示的指静脉验证特征提取和恢复
DOI：
10.1109/tifs.2017.2689724
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发表时间：
2017-08-01
影响因子：
6.8
通讯作者：
El-Yacoubi, Mounim A.
中科院分区：
文献类型：
--
作者：
Qin, Huafeng;El-Yacoubi, Mounim A.
手指静脉生物识别技术已经被广泛研究用于个人身份验证。尽管手指静脉验证的最新进展，目前的解决方案完全依赖于领域知识，仍然缺乏鲁棒性，从原始图像中提取手指静脉特征。本文提出了一种深度学习模型，利用有限的先验知识来提取和恢复静脉特征。首先，基于已知的国家的最先进的手工制作的手指静脉图像分割技术的组合，我们自动识别两个区域：一个明确的区域与手指静脉图案和背景之间的分离度高，和一个模糊的区域与它们之间的分离度低。第一个与所有上述分割技术分配相同分割标签（前景或背景）的像素相关联，而第二个对应于所有剩余像素。该方案用于自动丢弃模糊区域，并将清晰区域的像素标记为前景或背景。基于以标记像素为中心的补丁构建训练数据集。其次，在所得数据集上训练卷积神经网络（CNN）以预测每个像素为前景的概率（即，静脉像素）。CNN通过学习静脉图案和背景图案之间的差异来学习手指静脉图案是什么。然后，测试图像的任何区域中的像素可以被有效地分类。第三，我们提出了另一个新的和原始的贡献，通过开发和研究一个完全卷积的网络来恢复分割图像中丢失的手指静脉模式。在两个公开的手指静脉数据库上的实验结果表明，该方法在手指静脉验证准确率方面有了显著的提高。
Finger-vein biometrics has been extensively investigated for personal verification. Despite recent advances in finger-vein verification, current solutions completely depend on domain knowledge and still lack the robustness to extract finger-vein features from raw images. This paper proposes a deep learning model to extract and recover vein features using limited a priori knowledge. First, based on a combination of the known state-of-the-art handcrafted finger-vein image segmentation techniques, we automatically identify two regions: a clear region with high separability between finger-vein patterns and background, and an ambiguous region with low separability between them. The first is associated with pixels on which all the above-mentioned segmentation techniques assign the same segmentation label (either foreground or background), while the second corresponds to all the remaining pixels. This scheme is used to automatically discard the ambiguous region and to label the pixels of the clear region as foreground or background. A training data set is constructed based on the patches centered on the labeled pixels. Second, a convolutional neural network (CNN) is trained on the resulting data set to predict the probability of each pixel of being foreground (i.e., vein pixel), given a patch centered on it. The CNN learns what a finger-vein pattern is by learning the difference between vein patterns and background ones. The pixels in any region of a test image can then be classified effectively. Third, we propose another new and original contribution by developing and investigating a fully convolutional network to recover missing finger-vein patterns in the segmented image. The experimental results on two public finger-vein databases show a significant improvement in terms of finger-vein verification accuracy.