Enhancing Performance of Cancellable Fingerprint Biometrics Using Classifier Ensembles

Biometric systems automatically recognize individuals based on their physiological and/or behavioral characteristics like fingerprint, face, hand-geometry, iris, retina, palmprint, voice, gait, signature, and keystroke dynamics. These systems offer several advantages over traditional forms of identity protection (e.g. password-based). Nevertheless, many biometric characteristics are immutable, resulting in permanent compromise when a template is stolen. In order to replace compromised biometric templates, the concept of cancellable biometrics has recently been introduced. The concept is to transform a biometric trait into a new one for enrollment and matching. Although cancellable biometrics were proposed to solve privacy concerns, the concept raises new issues, since they make the authentication problem more complex and difficult to solve. Thus, more effective authentication structures are needed to perform these tasks. In this paper, we investigate the use of ensemble systems in cancellable biometrics, using fingerprint-based identification to illustrate the possible benefits accruing. In order to perform this analysis, we have proposed a simpler version of a transformation function used to create cancellable fingerprint. The main aim of this paper is to analyze the performance of such well-established structures on transformed biometric data to determine whether they have a positive effect on the performance of this complex and difficult task.