Q-stack: Uni- and Multimodal Classifier Stacking with Quality Measures

The use of quality measures in pattern classification has recently received a lot of attention in the areas where the deterioration of signal quality is one of the primary causes of classification errors. An example of such domain is biometric authentication. In this paper we provide a novel theoretical paradigm of using quality measures to improve both uniand multimodal classification. We introduce Q− stack, a classifier stacking method in which feature similarity scores obtained from the first classification step are used in ensemble with the quality measures as features for the second classifier. Using two-class, synthetically generated data, we demonstrate how Q− stack helps significantly improve both uniand multimodal classification in the presence of signal quality degradation.