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Efficient cancelable authentication system based on DRPE and adaptive filter.

Authors :
Naeem, Ensherah A.
Saied, Ayat
El-Fishawy, Adel S.
Rihan, Mohamad
Abd El-Samie, Fathi E.
El-Banby, Ghada M.
Source :
Multimedia Tools & Applications; Sep2024, Vol. 83 Issue 31, p76131-76175, 45p
Publication Year :
2024

Abstract

Currently, security enhancement of biometric systems is an important issue that deserves consideration. This is attributed to the threats facing traditional recognition systems, which depend on Personal Identification Numbers (PINs) that can be stolen, easily. Utilization of original biometrics to access user services may lead to loss of the biometrics forever, if hacking attempts succeed in gaining access to the storage database of original templates. To address this concern and to avoid the utilization of original biometrics, we keep them away from being compromised through the utilization of cancelable biometric templates. This paper introduces a novel methodology for user authentication with multiple biometrics to generate distorted non-invertible cancelable templates to be stored in the database. The proposed framework begins with Discrete Cosine Transform (DCT) to achieve data compression in a multi-biometric scenario. After that, Double Random Phase Encoding (DRPE) is applied to increase the security level of the generated templates. Finally, an adaptive filter is used to induce an effect of whitening to generate the cancelable biometric templates. The generated patterns are uncorrelated due to the effect of encryption and adaptive filtering, which improves the security level against identity theft and provides good performance. Simulation results prove a good performance of the proposed cancelable biometric recognition framework with an Area under the Receiver Operating Characteristic curve (AROC) of 52.12% and an Equal Error Rate (EER) of 44.8462%. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
13807501
Volume :
83
Issue :
31
Database :
Complementary Index
Journal :
Multimedia Tools & Applications
Publication Type :
Academic Journal
Accession number :
179414514
Full Text :
https://doi.org/10.1007/s11042-023-15013-9