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

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