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The growing deployment of biometrics as a proof of identity has generated a great deal of research into biometrics in recent years, and widened the scope of investigations beyond improving accuracy into mechanisms to deal with serious concerns raised about security and privacy due to the potential misuse. of the collected biometric data along with possible attacks on biometric systems. The focus on improving performance of biometric authentication has been more on multi-modal and multi-factor biometric authentication in conjunction with designing recognition techniques to mitigate the adverse effect of variations in recording conditions. Some of these approaches together with the emerging developments of cancellable biometrics and biometric cryptosystems have been used as mechanisms to enhance security and privacy of biometric systems. This thesis is designed to deal with these complimentary and closely related issues through investigations that aim at understanding the impact of varying biometric sample recording conditions on the discriminating information content (entropy) of these samples, and to use the gained knowledge to (1) design adaptive techniques for improved performance of biometric authentication, and (2) propose and test a framework for a proper evaluation of security of all factors/components involved in biometric keys and multi-factor biometric authentication. The first part of this thesis consists of a set of theoretical and empirical investigations designed to evaluate and analyse the effect of emerging developments in biometrics systems, with a focus on those related to biometric entropy and multi-factor authentication. The analysis of different biometric entropy measures, proposed in the literature, reveals that variations in biometric sample quality lead to variations in the correlation between biometric entropy values calculated using any of the known measures and the accuracy of the biometric recognition. Furthermore, analysis of the spatial distribution of entropy values in face images reveals a non-uniform distribution. The widely expected inherent individual differences in biometric features entropy will also be confirmed. Moreover, we uncover a myth reported in the literature about near perfect accuracy of certain quality-based adaptive recognition schemes.