Zhao Chengwei, Li Jian, Lin Min, Chen Xin, Liu Yang
IEEE Trans Ultrason Ferroelectr Freq Control. 2022 Oct;69(10):2965-2974. doi: 10.1109/TUFFC.2022.3198503. Epub 2022 Sep 27.
As an established biometric authentication approach, fingerprint scanning has received considerable attention due to its high accuracy and reliability. In this article, the fingerprint reconstruction at any position is achieved in large physical domains, which monitors wavefield variations of plate-like structures within arrays through the ultrasonic guided wave. Accurate reconstruction and quantitative characterization of fingerprints are obtained using fast inversion tomography (FIT) based on the deep learning convolutional neural network (DLCNN). Parametric optimization is conducted to reveal submillimeter fingerprint minutiae, and a specific DLCNN model is proposed for the artifact removal in FIT reconstructions. The results prove that the FIT based on DLCNN restoration can significantly improve the imaging quality in terms of increased resolution, reduced reconstruction errors, and higher fingerprint matching confidence. The reconstruction also allows an exponential improvement in computational efficiency as a result of much-reduced sensor numbers. Several factors affecting the performance of the proposed reconstruction method are discussed at the end.
作为一种成熟的生物特征认证方法，指纹扫描因其高准确性和可靠性而受到广泛关注。在本文中，通过超声波导波监测阵列内板状结构的波场变化，在大物理域中实现了任意位置的指纹重建。基于深度学习卷积神经网络（DLCNN）的快速反演层析成像（FIT）技术实现了指纹的精确重建和定量表征。通过参数优化揭示了亚毫米级的指纹细节特征，并提出了一种特定的DLCNN模型用于去除FIT重建中的伪影。结果表明，基于DLCNN恢复的FIT在提高分辨率、减少重建误差和提高指纹匹配置信度方面能够显著提高成像质量。由于传感器数量的大幅减少，重建还使计算效率得到指数级提升。最后讨论了影响所提出重建方法性能的几个因素。
