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).