Open Access

ARTICLE

An Efficient GCD-Based Cancelable Biometric Algorithm for Single and Multiple Biometrics

Naglaa F. Soliman^1,2, Abeer D. Algarni^1,*, Walid El-Shafai³, Fathi E. Abd El-Samie^1,3, Ghada M. El Banby⁴

1 Department of Information Technology, College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman University, Riyadh, 84428, Saudi Arabia
2 Department of Electronics and Communications, Faculty of Engineering, Zagazig University, Zagazig, 44519, Egypt
3 Department of Electronics and Electrical Communications, Faculty of Electronic Engineering, Menoufia University, Menouf, 32952, Egypt
4 Department of Industrial Electronics and Control Engineering, Faculty of Electronic Engineering, Menoufia University, Menouf, 32952, Egypt

* Corresponding Author: Abeer D. Algarni. Email:

Computers, Materials & Continua 2021, 69(2), 1571-1595. https://doi.org/10.32604/cmc.2021.016980

Received 17 January 2021; Accepted 10 April 2021; Issue published 21 July 2021

Abstract

Cancelable biometrics are required in most remote access applications that need an authentication stage such as the cloud and Internet of Things (IoT) networks. The objective of using cancelable biometrics is to save the original ones from hacking attempts. A generalized algorithm to generate cancelable templates that is applicable on both single and multiple biometrics is proposed in this paper to be considered for cloud and IoT applications. The original biometric is blurred with two co-prime operators. Hence, it can be recovered as the Greatest Common Divisor (GCD) between its two blurred versions. Minimal changes if induced in the biometric image prior to processing with co-prime operators prevents the recovery of the original biometric image through a GCD operation. Hence, the ability to change cancelable templates is guaranteed, since the owner of the biometric can pre-determine and manage the minimal change induced in the biometric image. Furthermore, we test the utility of the proposed algorithm in the single- and multi-biometric scenarios. The multi-biometric scenario depends on compressing face, fingerprint, iris, and palm print images, simultaneously, to generate the cancelable templates. Evaluation metrics such as Equal Error Rate (EER) and Area and Receiver Operator Characteristic curve (AROC) are considered. Simulation results on single- and multi-biometric scenarios show high AROC values up to 99.59%, and low EER values down to 0.04%.

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Citations