DeepPore: Fingerprint Pore Extraction Using Deep Convolutional Neural Networks

As technological developments have enabled high-quality fingerprint scanning, sweat pores, one of the Level 3 features of fingerprints, have been successfully used in automatic fingerprint recognition systems (AFRS). Since the pore extraction process is a critical step for AFRS, high accuracy is required. However, it is difficult to extract the pore correctly because the pore shape depends on the person, region, and pore type. To solve the problem, we have presented a pore extraction method using deep convolutional neural networks and pore intensity refinement. The deep networks are used to detect pores in detail using a large area of a fingerprint image. We then refine the pore information by finding local maxima to identify pores with different intensities in the fingerprint image. The experimental results show that our pore extraction method performs better than the state-of-the-art methods.
Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Issue Date
2017-12
Language
English
Keywords

HIGH-RESOLUTION

Citation

IEEE SIGNAL PROCESSING LETTERS, v.24, no.12, pp.1808 - 1812

ISSN
1070-9908
DOI
10.1109/LSP.2017.2761454
URI
http://hdl.handle.net/10203/227182
Appears in Collection
CS-Journal Papers(저널논문)
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