Chen Shuainan, Zhao Chengwei, Ren Jiahao, Li Jian, Chen Shili, Liu Yang
State Key Laboratory of Precision Measuring Technology and Instruments, Tianjin University, Tianjin 300072, China.
State Key Laboratory of Precision Measuring Technology and Instruments, Tianjin University, Tianjin 300072, China.
Ultrasonics. 2024 Aug;142:107350. doi: 10.1016/j.ultras.2024.107350. Epub 2024 May 24.
Fingerprint authentication is widely used in various areas. While existing methods effectively extract and match fingerprint features, they encounter difficulties in detecting wet fingers and identifying false minutiae. In this paper, a fast fingerprint inversion and authentication method based on Lamb waves is developed by integrating deep learning and multi-scale fusion. This method speeds up the inversion performance through deep fast inversion tomography (DeepFIT) and uses Mask R-CNN to improve authentication accuracy. DeepFIT utilizes fully connected and convolutional operations to approach the descent gradient, enhancing the efficiency of ultrasonic array reconstruction. This suppresses artifacts and accelerates sub-millimeter-level fingerprint minutia inversion. By identifying the overall morphological relationships of various minutia in fingerprints, meaningful minutia representing individual identities are extracted by the Mask R-CNN method. It segments and matches multi-scale fingerprint features, improving the reliability of authentication results. Results indicate that the proposed method has high accuracy, robustness, and speed, optimizing the entire fingerprint authentication process.
指纹认证在各个领域都有广泛应用。虽然现有方法能有效提取和匹配指纹特征，但在检测湿手指和识别假细节点方面存在困难。本文通过融合深度学习和多尺度融合，开发了一种基于兰姆波的快速指纹反转与认证方法。该方法通过深度快速反演层析成像（DeepFIT）加快反转性能，并使用Mask R-CNN提高认证准确率。DeepFIT利用全连接和卷积操作逼近下降梯度，提高超声阵列重建效率。这抑制了伪像并加速了亚毫米级指纹细节点反转。通过识别指纹中各种细节点的整体形态关系，利用Mask R-CNN方法提取代表个体身份的有意义的细节点。它对多尺度指纹特征进行分割和匹配，提高了认证结果的可靠性。结果表明，该方法具有高精度、鲁棒性和速度，优化了整个指纹认证过程。
