Multiple factors based evaluation of fingerprint images quality

Automatic assessment of Fingerprint Image Quality (FIQ) has significant influence on the performance of Automated Fingerprint Identification Systems (AFISs). Local texture and global texture clarity of fingerprint images are the main factors in the evaluation of FIQ. Available image size, dryness and Singular Points (SPs) of a fingerprint image are also considered as cofactors, each of them has different effect on the assessment of image quality. In this paper, Homogeneous-Zones-Divide is proposed to evaluate the global clarity of a fingerprint image. To be consistent with human perception of fingerprint images quality, the optimal weight is obtained by a constrained nonlinear optimization model. This optimal weight is further used to assess Composite Quality Score (CQS). Simulation on public database indicates that the precision of our method can achieve 97.5% of accurate rate and it can reasonably classify fingerprint images into four grades, which is helpful to improve the performance of (AFIS)