Anti-spoofing method for fingerprint recognition using patch based deep learning machine
Today's with increasing identity theft, biometric systems based on fingerprints have a growing importance in protection and access restrictions. Malicious users violate them by presenting fabricated attempts. For example, artificial fingerprints constructed by gelatin, Play-Doh and Silicone mol...
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| Main Authors: | , , |
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| Format: | Article |
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Elsevier
2020
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| Subjects: | |
| Online Access: | http://eprints.um.edu.my/24786/ https://doi.org/10.1016/j.jestch.2019.06.005 |
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| Summary: | Today's with increasing identity theft, biometric systems based on fingerprints have a growing importance in protection and access restrictions. Malicious users violate them by presenting fabricated attempts. For example, artificial fingerprints constructed by gelatin, Play-Doh and Silicone molds may be misused for access and identity fraud by forgers to clone fingerprints. This process is called spoofing. To detect such forgeries, some existing methods using handcrafted descriptors have been implemented for assuring user presence. Most of them give low accuracy rates in recognition. The proposed method used Discriminative Restricted Boltzmann Machines to recognize fingerprints accurately against fabricated materials used for spoofing. © 2019 Karabuk University |
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