Robust Minutiae Extractor: Integrating Deep Networks and Fingerprint Domain Knowledge

Dinh-Luan Nguyen, Kai Cao, Anil K. Jain
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引用次数: 68

Abstract

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: https://github.com/luannd/MinutiaeNet
融合深度网络和指纹领域知识的鲁棒细节提取器
我们提出了一种基于深度神经网络的全自动细节提取器MinutiaeNet,它具有紧凑的特征表示,用于快速比较细节集。具体来说,首先,一个名为CoarseNet的网络基于卷积神经网络和指纹领域知识(增强图像、方向场和分割图)估计细微点分数图和细微点方向。随后,另一个名为FineNet的网络根据分数图提炼候选的细节位置。我们证明了将指纹领域知识与深度网络结合使用的有效性。在潜在指纹(NIST SD27)和普通指纹(FVC 2004)公共领域指纹数据集上的实验结果为该方法的优点提供了全面的经验支持。此外,我们的方法找到了在精度和召回率方面比这两个数据集的最新技术更好的细节集。鉴于缺乏具有细节特征的注释指纹数据集,本文提出的鲁棒细节检测方法将有助于训练基于网络的指纹匹配算法以及大规模评估指纹个性。MinutiaeNet在Tensorflow中实现:https://github.com/luannd/MinutiaeNet
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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