Billions of fingerprint images are acquired and matched to protect the national borders and in a range of egovernance applications. Fast and accurate minutiae detection from fingerprint images is the key to advance fingerprint matching algorithms for large-scale applications. However, currently available fingerprint minutiae extraction methods are not accurate and fast enough to support such large-scale applications. This paper proposes a new method that uses a lightweight pixelwise local dilated neural network to extract local features and a patch-wise global neural network to recover the global features. It consolidates the local and global fingerprint features to generate a full-size minutiae location map, and then accurately localizes the minutiae positions by using a recursive connected components algorithm. We design a new loss function to accurately detect minutia orientation and incorporate a dynamic end-to-end loss to provide effective supervision in learning discriminant features. It is due to the proposed design and loss function that can enable higher accuracy with significantly less computations. We present reproducible experimental results from five publicly available contact-based and contactless databases that indicate significant improvement in the minutiae detection accuracy, which also leads to enhanced fingerprint matching accuracy. Since the minutiae represent key points in the fingerprint images, the proposed end-to-end minutiae detection method also has a potential to be employed in many other key points detection tasks.

中文翻译：

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局部检测，全局修补：用于高速准确检测指纹细节的端到端框架

获取并匹配数十亿个指纹图像以保护国界和一系列电子政务应用。从指纹图像中快速准确地检测细节是推进指纹匹配算法大规模应用的关键。然而，目前可用的指纹细节提取方法不够准确和快速，无法支持这种大规模应用。本文提出了一种新方法，使用轻量级像素级局部扩张神经网络提取局部特征，使用块级全局神经网络恢复全局特征。它整合了局部和全局指纹特征以生成全尺寸细节点位置图，然后使用递归连通分量算法准确定位细节点位置。我们设计了一种新的损失函数来准确检测细节方向，并结合动态端到端损失来提供学习判别特征的有效监督。这是由于所提出的设计和损失函数可以在显着减少计算量的情况下实现更高的精度。我们提供了来自五个公开可用的基于接触式和非接触式数据库的可重复实验结果，这些结果表明细节检测精度有了显着提高，这也导致了指纹匹配精度的提高。由于细节代表指纹图像中的关键点，因此所提出的端到端细节检测方法也有可能被用于许多其他关键点检测任务。