Model and Score Adaptation for Biometric Systems: Coping With Device Interoperability and Changing Acquisition Conditions

The performance of biometric systems can be significantly affected by changes in signal quality. In this paper, two types of changes are considered: change in acquisition environment and in sensing devices. We investigated three solutions: (i) model-level adaptation, (ii) score-level adaptation (normalisation), and (iii) the combination of the two, called “compound” adaptation. In order to cope with the above changing conditions, the model-level adaptation attempts to update the parameters of the expert systems (classifiers). This approach requires the authenticity of the candidate samples used for adaptation be known (corresponding to supervised adaptation), or can be estimated (unsupervised adaptation). In comparison, the score-level adaptation merely involves post processing the expert output, with the objective of rendering the associated decision threshold to be dependent only on the class priors despite the changing acquisition conditions. Since the above adaptation strategies treat the underlying biometric experts/classifiers as a black-box, they can be applied to any unimodal or multimodal biometric system, thus facilitating system-level integration and performance optimisation. Our contributions are: (i) proposal of compound adaptation; (ii) investigation and comparison of two different quality-dependent score normalisation strategies; and, (iii) empirical comparison of the merit of the above three solutions on the BANCA face (video) and speech database.