A Hybrid Quality Estimation Algorithm for Fingerprint Images

Estimating the quality of a fingerprint image is very important in an automatic fingerprint identification system. It helps to reject poor-quality samples during enrollment and adjust the enhancement, feature extraction and matching strategies according to the quality of fingerprints, thus upgrading the performance of the overall system. In this paper, we propose a locality sensitive algorithm for fingerprint image quality assessment. For low curvature parts, we estimate their quality based on the sparse coefficients computed against a redundant Gabor dictionary. For high curvature parts, the quality is measured with their responses of a set of symmetric descriptors. Besides, the ridge and valley clarity is evaluated for the whole foreground. By integrating these information, the quality assessment of a fingerprint image is obtained. We test the proposed method on the FVC2002 Db1 and FVC2004 Db1 databases. Experimental results demonstrate that the proposed method is an effective predictor of biometrics performance.