Anti-spoofing method for fingerprint recognition using patch based deep learning machine
Document Type
Article
Publication Date
1-1-2020
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
Keywords
Biometric systems, Deep learning, Discriminative Restricted Boltzmann Machines, Fingerprint authentication
Divisions
fsktm
Funders
Middle East University, Amman, Jordan
Publication Title
Enegineering Science and Technology-An International Journal-JESTECH
Volume
23
Issue
2
Publisher
Elsevier