Fingerprint Feature Extraction by Combining Texture, Minutiae, and
Frequency Spectrum Using Multi-Task CNN
- URL: http://arxiv.org/abs/2008.11917v1
- Date: Thu, 27 Aug 2020 05:15:39 GMT
- Title: Fingerprint Feature Extraction by Combining Texture, Minutiae, and
Frequency Spectrum Using Multi-Task CNN
- Authors: Ai Takahashi, Yoshinori Koda, Koichi Ito, Takafumi Aoki
- Abstract summary: We propose a novel CNN-based method for extracting fingerprint features from texture, minutiae, and frequency spectrum.
We show that the proposed method exhibits the efficient performance on fingerprint verification compared with a commercial fingerprint matching software and the conventional method.
- Score: 0.14337588659482517
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Although most fingerprint matching methods utilize minutia points and/or
texture of fingerprint images as fingerprint features, the frequency spectrum
is also a useful feature since a fingerprint is composed of ridge patterns with
its inherent frequency band. We propose a novel CNN-based method for extracting
fingerprint features from texture, minutiae, and frequency spectrum. In order
to extract effective texture features from local regions around the minutiae,
the minutia attention module is introduced to the proposed method. We also
propose new data augmentation methods, which takes into account the
characteristics of fingerprint images to increase the number of images during
training since we use only a public dataset in training, which includes a few
fingerprint classes. Through a set of experiments using FVC2004 DB1 and DB2, we
demonstrated that the proposed method exhibits the efficient performance on
fingerprint verification compared with a commercial fingerprint matching
software and the conventional method.
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