Biometric system performance can be improved by means of data fusion. Several kinds of
information can be fused in order to obtain a more accurate classification (identification or
verification) of an input sample. In this paper we present a method for computing the
weights in a weighted sum fusion for score combinations, by ...»»»»
Biometric system performance can be improved by means of data fusion. Several kinds of
information can be fused in order to obtain a more accurate classification (identification or
verification) of an input sample. In this paper we present a method for computing the
weights in a weighted sum fusion for score combinations, by means of a likelihood model.
The maximum likelihood estimation is set as a linear programming problem. The scores are
derived from a GMM classifier working on a different feature extractor. Our experimental
results assesed the robustness of the system in front a changes on time (different sessions)
and robustness in front a change of microphone. The improvements obtained were
significantly better (error bars of two standard deviations) than a uniform weighted sum or a
uniform weighted product or the best single classifier. The proposed method scales
computationaly with the number of scores to be fussioned as the simplex method for linear
programming.^^^^