A new fingerprint indexing algorithm for latent and non-latent impressions identification

Abstract

In this work, a new fingerprint identification algorithm for latent and non-latent impressions based on indexing techniques is presented. This proposal uses a minutia triplet state-of-the-art representation, which has proven to be very tolerant to distortions. Also, a novel strategy to partition the indexes is implemented, in the retrieving stage. This strategy allows to use the algorithm in both contexts, criminal and non-criminal. The experimental results show that in latent identification this approach has a 91.08% of hit rate at penetration rate of 20%, on NIST27 database using a large background of 267000 rolled impressions. Meanwhile in non-latent identification at the same penetration rate, the algorithm reaches a hit rate of 97.8% on NIST4 database and a 100% of hit rate on FVC2004 DB1A database. These accuracy values were reached with a high efficiency.