Adaptive Multibiometric Feature Fusion Based on Classification Distance Score

Matching score is one of the traditional fusion score metrics, but it’s not a good metric to classify the data with intra-class and inter-class scores. The classification confidence score can be used to well separate the data with intra-class score from the data with inter-class score, but it does not work well for the data whose matching scores are next to the classification threshold. Therefore, this paper proposes a new score metric based on the classification distance score, which contains not only the information of the first level of classification but also the information of the distance between matching score and classification threshold, and which can also increase the distance of the fusion scores between intra-class and inter-class scores, and the classification distance score provides the characteristics of effective discriminative information fusion set for fusion algorithm, which can improve the utilization rate of score metric; furthermore, since the information entropy indicates the information value of features, it can be used to define the feature correlation coefficient and feature weight coefficient, and then the weighted fusion and traditional SUM rules are unified in an adaptive algorithm framework, which can improve the fusion recognition rate. The experimental results indicate the validity of the proposed method.