We propose a fully automatic minutiae extractor, called MinutiaeNet, based on
deep neural networks with compact feature representation for fast comparison of
minutiae sets. Specifically, first a network, called CoarseNet, estimates the
minutiae score map and minutiae orientation based on convolutional neural
network and fingerprint domain knowledge (enhanced image, orientation field,
and segmentation map). Subsequently, another network, called FineNet, refines
the candidate minutiae locations based on score map. We demonstrate the
effectiveness of using the fingerprint domain knowledge together with the deep
networks. Experimental results on both latent (NIST SD27) and plain (FVC 2004)
public domain fingerprint datasets provide comprehensive empirical support for
the merits of our method. Further, our method finds minutiae sets that are
better in terms of precision and recall in comparison with state-of-the-art on
these two datasets. Given the lack of annotated fingerprint datasets with
minutiae ground truth, the proposed approach to robust minutiae detection will
be useful to train network-based fingerprint matching algorithms as well as for
evaluating fingerprint individuality at scale. MinutiaeNet is implemented in
Tensorflow: this https URL

提出了一种名为 MinutiaeNet 的全自动细节提取器，基于深度神经网络，采用紧凑特征表示，用于快速比较细节集。使用指纹领域知识和深度网络的结合，改善了细节检测的准确性，实验结果表明，MinutiaeNet 对 NIST SD27 和 FVC 2004 数据集上的细节集进行了更高精度和召回率的检测。该方法将有助于培训基于网络的指纹匹配算法，以及在大规模测量指纹个性时进行指纹分析。