Fingerprint indexing and matching: An integrated approach

Abstract

Large scale fingerprint recognition systems have been deployed worldwide not only in law enforcement but also in many civilian applications. Thus, it is of great value o identify a query fingerprint in a large background finger-print database both effectively and efficiently based on indexing strategies. The published indexing algorithms do not meet the requirements, especially at low penetrate rates, because of the difficulty in extracting reliable minutiae and other features in low quality fingerprint images. We propose a Convolutional Neural Network (ConvNet) based fingerprint indexing algorithm. An orientation field dictionary is learned to align fingerprints in a unified coordinate system and a large longitudinal fingerprint database, where each finger has multiple impressions over time, is used to train the ConvNet. Experimental results on NIST SD4 and NIST SD14 show that the proposed approach outperforms state-of-the-art fingerprint indexing techniques reported in the literature. Further indexing results on an augmented gallery set of 250K rolled prints demonstrate the scalability of the proposed algorithm. At a penetrate rate of 1%, a score-level fusion of the proposed indexing and a state-of-the-art COTS SDK provides 97.8% rank-1 identification accuracy with a 100-fold reduction in the search space.