Optical coherence tomography (OCT), as a non-destructive and high-resolution fingerprint acquisition technology, is robust against poor skin conditions and resistant to spoof attacks. It measures fingertip information on and beneath skin as 3D volume data, containing the surface fingerprint, internal fingerprint and sweat glands. Various methods have been proposed to extract internal fingerprints, which ignore the inter-slice dependence and often require manually selected parameters. In this article, a modified U-Net that combines residual learning, bidirectional convolutional long short-term memory and hybrid dilated convolution (denoted as BCL-U Net) for OCT volume data segmentation and two fingerprint reconstruction approaches are proposed. To the best of our knowledge, it is the first time that simultaneous and automatic extraction is performed for surface fingerprint, internal fingerprint and sweat gland. The proposed BCL-U Net utilizes the spatial dependence in OCT volume data and deals with segmentation of objects with diverse sizes to achieve accurate extraction. Comparisons have been performed to demonstrate the advantages of the proposed method. A thorough evaluation of the recognition abilities of internal and surface fingerprints is conducted using a dataset significantly larger than previous studies. Four databases containing internal and surface fingerprints are generated from 1572 OCT volume data by the proposed method. The internal fingerprint matching experiment has achieved a lowest equal error rate (EER) of 0.95%. Mixed internal and surface fingerprint matching experiment is also performed and achieves an EER of 3.67%, verifying the consistency of the internal and surface fingerprints. The matching experiments for fingers under poor skin conditions show a 2.47% EER of internal fingerprints that is much lower than that of surface fingerprints, which proves the advantage of internal fingerprints and indicates the potential of the internal fingerprints to supplement or replace the surface fingerprints for some specific applications.

中文翻译：

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通过卷积神经网络从光学相干断层扫描重建表面和内部指纹


光学相干断层扫描（OCT）作为一种无损、高分辨率的指纹采集技术，能够抵御恶劣的皮肤条件并抵抗欺骗攻击。它以 3D 体积数据的形式测量皮肤上和皮肤下的指尖信息，包括表面指纹、内部指纹和汗腺。人们已经提出了各种方法来提取内部指纹，这些方法忽略了切片间的依赖性，并且通常需要手动选择参数。在本文中，提出了一种结合残差学习、双向卷积长短期记忆和混合扩张卷积（表示为 BCL-U Net）的改进 U-Net，用于 OCT 体积数据分割和两种指纹重建方法。据我们所知，这也是首次对表面指纹、内部指纹和汗腺进行同步自动提取。所提出的 BCL-U Net 利用 OCT 体积数据中的空间依赖性并处理不同尺寸的对象分割以实现准确提取。进行了比较以证明所提出方法的优点。使用比以前的研究大得多的数据集对内部和表面指纹的识别能力进行了全面评估。通过所提出的方法从 1572 个 OCT 体积数据生成四个包含内部和表面指纹的数据库。内部指纹匹配实验取得了最低0.95%的等错误率（EER）。还进行了内部和表面指纹混合匹配实验，EER达到3.67%，验证了内部和表面指纹的一致性。 恶劣皮肤条件下的手指匹配实验表明，内部指纹的EER为2.47%，远低于表面指纹，这证明了内部指纹的优势，表明内部指纹补充或替代表面指纹的潜力。一些具体的应用。