Multimodal Cancelable Biometric System
Abstract
Cyberattacks against individuals and organizations are increasing at alarming rates all over the world. Traditional password, pin, smart id, and tokens based systems are insufficient to provide reliable authentication. Biometric authentication is now widely used in both physical and virtual worlds. Unfortunately, even the well-established biometric systems are suffering from vulnerabilities, with the most crucial components being biometric templates that store user biometric data. If the biometric of a user is compromised, the identity and privacy of an individual are compromised as well, since it is impossible to revoke or reissue the biometric template. Therefore, secure and revocable biometric template generation algorithms are required to ensure biometric system integrity and user privacy. In this thesis, biometric template generation algorithm for multimodal biometric system is presented. To achieve the template protection for the multimodal biometric verification system, new methods for feature level fusion and feature extraction are proposed, called Cancelable Feature Fusion (CFF) and Cancelable Binary Pattern (CBP), respectively. CFF combines multiple biometric traits using random indexes so that for every fusion it generates a new fused template. Developed 2-Fold Cross-Folding (2-CF) and Generalized Cross-Folding (G-CF) are new algorithms for cancelable feature fusion, which utilize random indexes to combine multiple biometric traits. Another developed method is CBP, a biometric feature extraction algorithm that can generate a new set of features from a single sample as needed. For after matching fusion, Social Network Analysis (SNA) based score fusion is proposed to achieve better verification accuracy. In this thesis, traditional feature fusion and feature extraction algorithms are replaced with CFF and CBP respectively to support the template protection. For the validation of the methodologies, genuine, stolen and fake key scenarios are analyzed using several sets of virtual multimodal biometric databases of face, ear, and signature. Experimental results show that proposed Multimodal Cancelable Biometric System (MCBioS) architectures can achieve 0% Equal Error Rate (EER) and a higher template integrity.
Description
Keywords
Computer Science
Citation
Paul, P. P. (2016). Multimodal Cancelable Biometric System (Doctoral thesis, University of Calgary, Calgary, Canada). Retrieved from https://prism.ucalgary.ca. doi:10.11575/PRISM/27029