Improving Single Modal and Multimodal Biometric Authentication Using F-ratio Client-Dependent Normalisation
This study investigates a new client-dependent normalisation to improve a single biometric authentication system, as well as its effects on fusion. There exists two families of client-dependent normalisation techniques, often applied to speaker authentication. They are client-dependent score and threshold normalisation techniques. Examples of the former family of techniques are Z-Norm, D-Norm and T-Norm. There is also a vast amount of literature on the latter family of techniques. Both families are surveyed in this study. Furthermore, we also provide a link between these two families of techniques and show that one is a dual representation of the other. These techniques are intended to adjust the variation across different client models. We propose ``F-ratio'' normalisation, or F-Norm, applied to face and speaker authentication systems in two contexts: single modal and fusion of multi-modal biometerics. This normalisation requires that only as few as two client-dependent accesses are available (the more the better). Different from previous normalisation techniques, F-Norm considers the client and impostor distributions simultaneously. We show that F-ratio is a natural choice because it is directly associated to Equal Error Rate. It has the effect of centering the client and impostor distributions such that a global threshold can be easily found. Another difference is that F-Norm actually ``interpolates'' between client-independent and client-dependent information by introducing two mixture parameters. These parameters can be optimised to maximise the class dispersion (the degree of separability between client and impostor distributions) while the aforementioned normalisation techniques cannot. The results of 13 single modal experiments and 32 fusion experiments carried out on the XM2VTS multimodal database show that in both contexts, F-Norm is advantageous over Z-Norm, client-dependent score normalisation with EER and no normalisation.