Fingerprint quality assessment using a no-reference image quality metric

Abstract

The quality assessment of the acquired biometric raw data is very important as it deeply affects the performance of biometric systems and consequently their usability. Poor quality samples increase the enrolment failures, and decrease the system performance. In this paper, we present a new quality assessment metric of fingerprints. Its main originality lies in the use of a no-reference image quality metric. The proposed quality metric combines two types of parameters through a weighted sum optimized by a genetic algorithm: 1) image quality criterion and 2) pattern-based quality criteria (salient and patch-based features). BOZORTH3 matching system and the FVC2002 DB3 fingerprint database are used to clarify the benefits of the presented quality metric.