An Efficient PSO Optimized Integration Weight Estimation Using D-prime Statistics for A Multibiometric System

The efficiency 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 efficient PSO (Particle Swarm Optimization) based integration weight optimization scheme using d-prime statistics to determine the optimal weight factors 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 method is analysed in the context of fingerprint and voice biometrics using sum rule of fusion. The d-prime statistics of fingerprint and voice modalities are measured and the integration weights are computed as the ratio of these two statistics. The computed d-prime ratio is then optimized against the recognition accuracy. The optimizing parameter is estimated in the training/validation phase using Leave-One-Out Cross Validation (LOOCV) technique. Experimental studies show that the proposed method improves recognition rate under normal operating conditions and reduces the FAR (False Acceptance Rate) considerably even at extreme low SNR (Signal to Noise Ratio) conditions. The proposed biometric solution can be easily integrated into any multibiometric system with score level fusion. Moreover, it finds extremely useful in applications where there are less number of available training samples.