Abstract As a popular living fingerprint feature, sweat pore has been adopted to build robust high resolution automated fingerprint recognition systems (AFRSs). Pore matching is an important step in high resolution fingerprint recognition. This paper proposes a novel pore matching method with high recognition accuracy. The method mainly solves the pore representation problem in the state-of-the-art direct pore matching method. By making full use of the diversity and large quantities of sweat pores on fingerprints, deep convolutional networks are carefully designed to learn a deep feature (denoted as DeepPoreID) for each pore. The inter-class difference and intra-class similarity of pore patch pairs can be well solved using deep learning. The DeepPoreID is then used to describe the local feature for each pore and finally integrated into the classical direct pore matching method. More specifically, pore patches, which are cropped from both Query and Template fingerprint images, are imported into the well-trained networks to generate DeepPoreID for pore representation. The similarity between those DeepPoreIDs are then obtained by calculating the Euclidian Distance between them. Subsequently, one-to-many coarse pore correspondences are established via comparing their similarity. Finally, classical Weighted RANdom SAmple Consensus (WRANSAC) is employed to pick true pore correspondences from coarse ones. The experiments carried on the two public high resolution fingerprint database have shown the effectiveness of the proposed DeepPoreID, especially for fingerprint matching with small image size. Meanwhile, better recognition accuracy is achieved by the proposed method when compared with the existing state-of-the-art methods. About 35% rise in equal error rate (EER) and about 30% rise in FMR1000 when compared with the best result evaluated on the database with image size of 320 × 240 pixels.

中文翻译：

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使用深层特征进行指纹毛孔匹配

摘要 作为一种流行的活体指纹特征，汗孔已被用于构建强大的高分辨率自动指纹识别系统（AFRS）。孔匹配是高分辨率指纹识别的重要步骤。本文提出了一种具有高识别精度的新型孔隙匹配方法。该方法主要解决目前最先进的直接孔隙匹配方法中的孔隙表示问题。通过充分利用指纹上汗孔的多样性和大量汗孔，精心设计深度卷积网络，为每个孔学习一个深度特征（表示为DeepPoreID）。使用深度学习可以很好地解决孔隙补丁对的类间差异和类内相似性。然后使用 DeepPoreID 来描述每个孔隙的局部特征，并最终集成到经典的直接孔隙匹配方法中。更具体地说，从查询和模板指纹图像中裁剪出的孔隙补丁被导入训练有素的网络中以生成用于孔隙表示的 DeepPoreID。然后通过计算它们之间的欧几里得距离来获得这些 DeepPoreID 之间的相似性。随后，通过比较它们的相似性建立一对多的粗孔对应关系。最后，采用经典的加权随机样本共识（WRANSAC）从粗略的孔隙对应中挑选真正的孔隙对应。在两个公开的高分辨率指纹数据库上进行的实验表明了所提出的 DeepPoreID 的有效性，特别适用于小图像大小的指纹匹配。同时，与现有的最先进方法相比，所提出的方法实现了更好的识别精度。与在图像大小为 320 × 240 像素的数据库上评估的最佳结果相比，等错误率 (EER) 增加了约 35%，FMR1000 增加了约 30%。