Fusion of Digital Fingerprint Quality Assessment Metrics

The quality assessment of biometric samples is a crucial issue in biometrics, indeed, many studies showed its significant impact on the subsequent performance of the biometric system. Many metrics have been proposed and studied in the literature in order to quantify their usefulness. In this paper, we propose to merge different metrics in order to improve the utility estimation of the quality assessment. We use the enrollment selection validation approach in order to compute the utility estimation of the fused metrics. We show the efficiency of the proposed approach comparing with 7 well known metrics on the 12 FVC datasets and 5 synthesized SFinGE-based databases with two matching algorithms. Experimental results show a good improvement on the fused metric to better qualify the quality of digital fingerprints. Those results demonstrate the effectiveness of the approach.