Biometric Keys for the Encryption of Multimodal Signatures

Abstract

Biometrics have long been used as means to recognize people, mainly in terms of their physiological characteristics, for various commercial applications ranging from surveillance and access control against potential impostors to smart interfaces (Qazi (2004)) (Xiao (2005)). These systems require reliable personal recognition schemes to either confirm or determine the identity of an individual requesting their services. The biometric methods, that are usually incorporated in such systems, can be categorized to physiological and behavioral (Jain et al. (2004)), depending on the type of used features. The most popular physiological biometric traits are the fingerprint (Maltoni et al. (2009)) that is widely used in law enforcement for identifying criminals, the face (Chang et al. (2005)) and the iris (Sun & Tan (2009)). However, despite their high recognition performance, static biometrics have been recently overwhelmed by the new generation of biometrics, which tend to cast light on more natural ways for recognizing people by analyzing behavioural traits. Specifically, behavioural biometrics are related to specific actions and the way that each person executes them. In other words, they aim at recognizing livingness, as it is expressed by dynamic traits. The most indicative cases of behavioural biometric recognition is gait (Goffredo et al. (2009b)), facial expressions (Liu & Chen (2003)) or other activity related, habitual traits (Drosou, Ioannidis, Moustakas & Tzovaras (2010)). As a result behavioural biometrics have become much more attractive to researchers due to their significant recognition potential and their unobtrusive nature. They can potentially allow the continuous (on-the-move) authentication or even identification unobtrusively to the subject and become part of an Ambient Intelligence (AmI) environment. The inferior performance of behavioural biometrics, when compared to the classic physiological ones, can be compensated when they are combined in a multimodal biometric system. In general, multimodal systems are considered to provide an excellent solution to a series of recognition problems. Unimodal systems are more vulnerable to theft attempts, since an attacker can easily gain access by stealing or bypassing a single biometric feature. In the same concept, they have to contend with a variety of problems, such as noisy data, intraclass variations, restricted degrees of freedom, non-universality, spoof attacks, and unacceptable error rates, i.e., it is estimated that approximately 3% of the population does not have legible 8