Cross-Sensor Fingerprint Matching Using Siamese Network and Adversarial Learning
Autor: | Ashwaq Alotaibi, Adhwa Alrashidi, Muhammad Hussain, George Bebis, Hatim Aboalsamh, Helala AlShehri |
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Rok vydání: | 2021 |
Předmět: |
biometrics
Matching (statistics) Biometrics Computer science Interoperability 0211 other engineering and technologies Fingerprint Verification Competition TP1-1185 02 engineering and technology computer.software_genre Biochemistry Article Analytical Chemistry 020204 information systems 0202 electrical engineering electronic engineering information engineering cross-sensor fingerprint matching Electrical and Electronic Engineering Instrumentation 021110 strategic defence & security studies Authentication Chemical technology Fingerprint (computing) Siamese network Atomic and Molecular Physics and Optics GAN Identity (object-oriented programming) Benchmark (computing) Data mining computer CNN adversarial learning |
Zdroj: | Sensors (Basel, Switzerland) Sensors Volume 21 Issue 11 Sensors, Vol 21, Iss 3657, p 3657 (2021) |
ISSN: | 1424-8220 |
Popis: | The fingerprint is one of the leading biometric modalities that is used worldwide for authenticating the identity of persons. Over time, a lot of research has been conducted to develop automatic fingerprint verification techniques. However, due to different authentication needs, the use of different sensors and the fingerprint verification systems encounter cross-sensor matching or sensor interoperability challenges, where different sensors are used for the enrollment and query phases. The challenge is to develop an efficient, robust and automatic system for cross-sensor matching. This paper proposes a new cross-matching system (SiameseFinger) using the Siamese network that takes the features extracted using the Gabor-HoG descriptor. The proposed Siamese network is trained using adversarial learning. The SiameseFinger was evaluated on two benchmark public datasets FingerPass and MOLF. The results of the experiments presented in this paper indicate that SiameseFinger achieves a comparable performance with that of the state-of-the-art methods. |
Databáze: | OpenAIRE |
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