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Biometric acquired and processed data quality is the prime influences which will affect the performance of the whole biometric system. Hence, aforementioned is essential to control the quality of acquired data to devise a suitable biometric system. This paper presents a robust multimodal biometric system using quality dependent expert fusion system. We Presents work, on a novel quality assessment metrics for Fingerprint, Palmprint, and Iris. The originality of this work contributing with blind image quality measures. The projected quality metrics associates with two type of quality measure a) Image-based quality as well as b) pattern-based. We have explore and comprehend the associated various quality assessment in the biometrics. Benefits of the proposed quality matric have been illustrates on six benchmark database. The performance of the proposed quality measures demonstrates on multimodal biometric system is evaluated on a public dataset and demonstrating its recognition accuracy with respect to EER. Result shows the efficiency of detecting the kind of alterations. Kolmogorov-Smirnov (KS) test statistics shows 0.84 to 0.94 outperformed as compared to NFIQ.

Quality-dependent fusion system using no-reference image quality metrics for multimodal biometrics