An effective template protection method for face and voice cancellable identification

Abstract

Cancellable biometrics adopt an approach in which biometric templates can be cancelled and replaced if compromised, for example if they are lost or stolen, and can therefore overcome some of the security concerns about biometric-based authentication systems. This paper proposes a simple and effective template protection method (cancellable transformation) for providing cancellable data, called the double sum (DS) method, which performs a sum procedure over the attributes that makes the definition of the original data a hard (and almost impossible) process. In order to investigate the feasibility of the proposed method, an empirical analysis is conducted and we use as examples two different biometric modalities (face and voice) separately and in a multi-modal context (multi-biometric). The main aim of this paper is to provide greater security and improved performance in the biometric authentication process. The datasets used in this analysis were TIMIT for voice and the AR Face dataset for face. As a result of this analysis, we will observe that the proposed transformation offered higher performance to two existing cancellable transformations.