Robust cancellable biometrics scheme based on neural networks. Issue 3 (1st September 2016)

• Record Type: Journal Article
• Authors: Tarek, Mayada; Ouda, Osama; Hamza, Taher
• Is Part Of: IET biometrics. Volume 5:Issue 3(2016)
• Page Start: 220
• Page End: 228
• Publication Date: 2016-09-01
• Subjects: biometrics (access control) -- neural nets -- data privacy -- convolution; robust cancellable biometrics scheme -- neural networks -- CB -- biometric data protection -- user privacy -- brute force -- pre‐image attacks -- correlation attacks -- convolution operation -- bidirectional associative memory -- BAM neural network -- random bit‐strings -- generated cancellable template -- cancellable transformation process -- biometric template -- blind deconvolution; Biometric identification -- Periodicals; 570.15195
• DOI: 10.1049/iet-bmt.2015.0045
• Languages: English
• ISSNs: 2047-4938

Abstract

Abstract : Several cancellable biometrics (CBs) techniques have been proposed to protect biometric data and maintain users' privacy. Although such techniques can withstand brute‐force and/or pre‐image attacks, they are vulnerable to correlation attacks. In this study, the authors propose a novel correlation attack‐resistant CBs scheme that is based on a convolution operation and a bidirectional associative memory (BAM) neural network. The proposed scheme utilises BAM to bind biometric templates to random bit‐strings in a secure and efficient manner. These random bit‐strings are then employed to derive cancellable templates from the true templates linked to them via BAM weights, which are safely stored with the generated cancellable template in the system database. In this study, linear convolution is adopted as the cancellable transformation process. The result of convolving the original biometric template with the transformation key is binarised according to a predefined threshold to thwart blind de‐convolution. The security of the proposed scheme against different attacks is analysed and experiments on the CASIA‐IrisV3‐Interval dataset illustrate the efficacy of the proposed scheme.