Fingerprint image quality assessment based on BP neural network with hierarchical clustering

Fingerprint image quality assessment is important because the good performance of the minutiae-based matching algorithm is heavily dependent on fingerprint images with high quality. Many efforts have been made in existing methods, but most methods either use full fingerprint images or use local areas and involve subjective judgments. Unlike previous methods, the proposed method considers both local and global assessments. Local feature vectors are extracted from the fingerprint image block for hierarchical clustering, and the results are used as target outputs of the back-propagation (BP) neural network without any subjective judgments. Global feature vectors based on the local quality assessment results are used for hierarchical clustering and fed into the BP neural network that calculates the overall error rate of genuine and imposter errors to achieve global quality assessment. Furthermore, the minutiae quality assessment method is also proposed and incorporated into the minutiae-based matching algorithm. The experimental results based on the FVC2002 and FVC2004 databases show that the proposed methods can effectively assess the quality of fingerprint images and ensure the overall improvement of matching performance.