Journal
PATTERN RECOGNITION
Volume 102, Issue -, Pages -Publisher
ELSEVIER SCI LTD
DOI: 10.1016/j.patcog.2020.107208
Keywords
Fingerprint recognition; Pore representation; Direct pore matching; Convolutional neural networks
Funding
- Natural Science Foundation of China [61672357, 61703283 andU1713214]
- Shenzhen Fundamental Research fund [JCYJ20180305125822769, JCYJ20160331185006518, JCYJ20160530141902978]
- Tencent Rhinoceros Birds-Scientific Research Foundation for Young Teachers of Shenzhen University
- Science and Technology Funding of Guangdong Province [2018A050501014]
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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 x 240 pixels. (C) 2020 Elsevier Ltd. All rights reserved.
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