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. The authors investigated three solutions: model-level adaptation, score-level adaptation (normalization), and 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).