Optimization of Integration Weights for a Multibiometric System with Score Level Fusion
The effectiveness of a multibiometric system can be improved by weighting the scores obtained from the degraded modalities in an appropriate manner. In this paper, we propose an integration weight optimization scheme to determine the optimal weight factor for the complementary modalities, under different noise conditions. Instead of treating the weight estimation process from an algebraic point of view, an attempt is made to consider the same from the principles of linear programming techniques. The performance of the proposed technique is analysed in the context of fingerprint and voice biometrics using sum rule of fusion. The weight factor is optimized against the recognition accuracy. The optimizing parameter is estimated in the training/ validation phase using Leave-One-Out Cross Validation (LOOCV) technique. The proposed biometric solution can be be easily integrated into any multibiometric system with score level fusion. More over, it finds extremely useful in applications where there are less number of available training samples.
KeywordsGaussian Mixture Model Recognition Accuracy Integration Weight False Rejection Rate Score Level Fusion
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