A linear convolution-based cancelable fingerprint biometric authentication system

Abstract

Authentication is a critical requirement of many systems, in domains such as law enforcement, financial services and consumer devices. Due to poor user practices, especially regarding passwords, biometric technologies have been presented as a viable solution, and have been constantly evolving to meet this requirement. It is important to consider the security aspects of any proposed biometric authentication system, as threats targeting biometric template data are serious. Given that the original biometric data are not revocable, if compromised, they are lost (or tainted) forever. To prevent biometric template data from being compromised by attackers, we propose a new linear convolution-based cancelable fingerprint authentication system. In the proposed system, instead of using the original feature data themselves as the inputs to the linear convolution function, the second input is replaced by a help vector, which guarantees that errors from one part of the template data do not impact other parts. Moreover, to ensure the safety of the help vector chosen from a help vector pool in the lost-key scenario, a feature-guided index generation algorithm is developed. The experimental results show that the proposed system achieves satisfactory recognition accuracy, while providing strong protection to fingerprint template data.