Dynamic score combination of binary experts

Abstract

The combination of experts is used to improve the performance of a classification system. In this paper we propose three dynamic score combination techniques that embed the selection and the fusion approach for combining experts. The proposed techniques are designed to combine binary experts that output a score measuring the degree of similarity to the positive class. Reported results on two biometric dataset show the effectiveness of the proposed techniques in terms of AUC and EER

An Evaluation Survey of Score Normalization in Multibiometric Systems

“…The scores between two extremities of the overlap region are mapped using two linear functions separately in range of [0, 1] and of [1,2] towards left and right of t, respectively as equation (4).…”

“…GAR(FAR=0.001 or else) is another performance measure which is also widely used in biometric performance evaluation [1]. In ROC analysis the Area Under the Curve (AUC) [2] is the also used evaluate the performance of a two-class system because it is a more discriminating measure than the accuracy. In biometric recognition systems, we always try to make EER smaller and GAR(FAR=0.001) as well as AUC larger.…”

“…Four Segment Piecewise-Linear(FSPL) [6] technique divides the regions of impostor and genuine scores into four segments and map each segment using piecewise linear functions. The scores between two extremities of the overlap region are mapped using two linear functions separately in range of [0, 1] and of [1,2] towards left and right of t, respectively as equation (4). min( ) 0 ( min( )) / ( min( ))…”