Customizing Biometric Authentication Systems via Discriminative Score Calibration

There is mounting evidence about the benefit of tailoring
a biometric authentication system to each user by postprocessing
the system output at the score level, also known
as client-specific score normalisation. Examples of these
procedures are Z-norm and F-norm. These procedures can
calibrate the uneven hypothesis space such that the dispropotionate
false acceptance and false rejection errors are
reduced after the calibration. The interest in studying these
schemes is that they are applicable to any biometric authentication
system regardless of the underlying biometric
modality, and furthermore, potentially be extended to object
recognition framed as a verification problem. We propose
to further improve these procedures by adding additional
client-specific terms that cannot be incorporated easily
in their respective existing form. Experiments carried out
on 13 face and speech systems show that both variants systematically
outperform their respective score normalisation
scheme (Z-norm or F-norm).