An Iterative Deep Neural Network for Hand-Vein Verification
An Iterative Deep Neural Network for Hand-Vein Verification
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DOI：
10.1109/access.2019.2901335
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发表时间：
2019-02
期刊：
影响因子：
3.9
通讯作者：
Huafeng Qin;M. El-Yacoubi;Jihai Lin;Bo Liu
中科院分区：
文献类型：
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作者：
Huafeng Qin;M. El-Yacoubi;Jihai Lin;Bo Liu
手静脉生物特征识别作为一种高安全性的模式越来越受到人们的重视。手静脉验证中的一个公开问题是缺乏对图像质量劣化的鲁棒性，这可能包括验证准确性。为了实现鲁棒性验证，静脉特征提取方法，特别是静脉纹理分割，已被广泛研究。近年来，深度神经网络在医学图像分割方面取得了可喜的成果，并已被引入静脉验证，但目前的解决方案在静脉分割方面面临两大挑战：1）缺乏标记数据，这是昂贵的获得和2）通过手动标记方案或自动标记方案获得的不正确的标记数据可能强烈影响网络训练时的参数，这可能降低验证性能。本文提出了一种基于初始标签数据的迭代深度信念网络（DBN）来提取静脉特征，这些特征是使用非常有限的先验知识自动生成的，并由我们的DBN迭代校正。首先，采用已知的手工静脉图像分割技术来自动标记静脉像素和背景像素。基于以标记像素为中心的补丁构建训练数据集。第二，DBN在所得到的数据库上进行训练，以预测每个像素属于静脉像素的概率，给定以其为中心的补丁。使用概率阈值0.5分割静脉图案。得到的静脉特征用于重建训练数据集，基于该训练数据集重新训练网络。在迭代过程中，训练数据的不正确标签被统计校正，这使得DBN能够通过学习静脉模式与背景模式之间的差异来有效地学习手指静脉模式。在两个公开的手静脉数据库上的实验结果表明，手静脉验证的准确性方面有显着的改善。
Hand-vein biometrics as a high-security pattern has received more and more attention. One of the open issues in hand-vein verification is the lack of robustness against image quality degradation, which may comprise the verification accuracy. To achieve robust verification, vein feature extraction approaches, especially vein texture segmentation, have been extensively investigated. In recent years, deep neural networks have achieved promising results in medical image segmentation and have been brought into vein verification, but current solutions suffer from two challenges for vein segmentation: 1) lacking the labeling data, which is expensive to obtain and 2) the incorrect label data obtained by manual labeling scheme or automatic labeling scheme may strongly influence parameters when the network is trained, which may degrade the verification performance. This paper proposes an iterative deep belief network (DBN) to extract vein features based on the initial label data, which are automatically generated using a very limited a priori knowledge and iteratively corrected by our DBN. First, a known handcrafted vein image segmentation technique is employed to automatically label vein pixel and background pixel. A training dataset is constructed based on the patches centered on the labeled pixels. Second, a DBN is trained on the resulting database to predict the probability of each pixel to belong to be a vein pixel given a patch centered on it. The vein patterns are segmented using a probability threshold of 0.5. The resulting vein features are employed to reconstruct the training dataset, based on which the network is retrained. During the iterative procedure, the incorrect labels of training data are statistically corrected, which enables DBN to effectively learn what a finger-vein pattern is by learning the difference between vein patterns and background ones. The experimental results on two public hand-vein databases show a significant improvement in terms of hand-vein verification accuracy.