دانلود گزارش کارورزی و کار آموزی

چکیده : 

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-tonoise

ratio (SNR) conditions. The proposed biometric solutions will be extremely useful in applications

where there are less number of available training samples.