Uliyan, Diaa Mohammed and Sadeghi, Somayeh and Jalab, Hamid Abdullah (2020) Anti-spoofing method for fingerprint recognition using patch based deep learning machine. Engineering Science and Technology, an International Journal, 23 (2). pp. 264-273. ISSN 2215-0986, DOI https://doi.org/10.1016/j.jestch.2019.06.005.
Full text not available from this repository.Abstract
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
| Item Type: | Article |
|---|---|
| Funders: | Middle East University, Amman, Jordan |
| Uncontrolled Keywords: | Biometric systems; Deep learning; Discriminative Restricted Boltzmann Machines; Fingerprint authentication |
| Subjects: | Q Science > QA Mathematics > QA75 Electronic computers. Computer science T Technology > T Technology (General) |
| Divisions: | Faculty of Computer Science & Information Technology |
| Depositing User: | Ms. Juhaida Abd Rahim |
| Date Deposited: | 11 Jun 2020 03:07 |
| Last Modified: | 11 Jun 2020 03:07 |
| URI: | http://eprints.um.edu.my/id/eprint/24786 |
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