Adaptive score level fusion of fingerprint and voice combining wavelets and separability measures

This paper presents an adaptive combinational approach for the score level fusion of fingerprint and voice biometrics, whose performance under adverse noise conditions are investigated systematically. An efficient preprocessing on the raw vector of scores using normalization and wavelet denoising is proposed, to improve the performance of the multibiometric system. The class as well as the score separability measures, under various noise conditions are estimated and combined algebraically, to determine the best integration weights, for the complementary modalities employed. The z-score normalized impostor scores are modelled as white Gaussian noise so that the wavelet denoising techniques can be readily applied. The inter/intra class separability measures from the feature space and the d-prime separability measures from the match score space are estimated in the training/validation phase. The performance of the proposed method is compared with the baseline techniques on score level fusion. Experimental evaluations show that the proposed method improves the recognition accuracy and reduces the false acceptance rate (FAR) and false rejection rate (FRR) over the baseline systems, under various signal-to-noise ratio (SNR) conditions. The proposed biometric solutions will be extremely useful in applications where there are less number of available training samples.