Fingerprint image quality assessment and scoring using minutiae centered local patches

Abstract

Performance of an automatic fingerprint identification system (AFIS) depends on the quality of fingerprint images. Therefore, quality estimation of fingerprint images can lead to performance enhancement of AFIS by eliminating the poor quality fingerprint images. A fingerprint quality estimation algorithm is proposed, which computes the fingerprint image quality at local level (blockwise). The proposed quality estimation algorithm analyzes blocks of fingerprint images in terms of their quality nature (dry, normal dry, good, normal wet, and wet). The features used for quality nature assessment are moisture, mean, variance, ridge valley area uniformity, and ridge line count. The performance of the block quality nature is assessed on fingerprint verification competition (FVC) 2004 datasets using a decision tree classifier. The proposed approach achieves classification accuracy of 95.90%. Further, the overall quality score (QS) for a fingerprint image is obtained by combining QSs assigned to all minutiae centered local patches of the fingerprint image using quality nature assessment, orientation analysis, and clarity analysis. The overall QSs for fingerprint images of FVC 2004 database (DB1, DB2, DB3, and DB4 datasets) are computed. These scores are used to evaluate the quality ranked recognition performance on each dataset of FVC 2004 database. Experimental evaluations reveal that rejecting low quality fingerprint images improves the performance of the recognition system. A comparative study with state-of-the-art quality estimation algorithms indicates that the QSs assigned using the proposed method are accurate and precise. Therefore, the proposed method can be used as a quality control unit during fingerprint acquisition, which helps in improving the performance of a recognition algorithm.