Quality dependent fusion of intramodal and multimodal biometric experts
2007
We address the problem of score level fusion of intramodal and multimodal experts in the context of biometric
identity verification. We investigate the merits of confidence based weighting of component experts. In contrast
to the conventional approach where confidence values are derived from scores, we use instead raw measures of
biometric data quality to control the influence of each expert on the final fused score. We show that quality based
fusion gives better performance than quality free fusion. The use of quality weighted scores as features in the
definition of the fusion functions leads to further improvements. We demonstrate that the achievable performance
gain is also affected by the choice of fusion architecture. The evaluation of the proposed methodology involves
6 face and one speech verification experts. It is carried out on the XM2VTS data base.
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