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.
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Hernández-Palancar, J., Muñoz-Briseño, A. (2015). A New Fingerprint Indexing Algorithm for Latent and Non-latent Impressions Identification. In: Pardo, A., Kittler, J. (eds) Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications. CIARP 2015. Lecture Notes in Computer Science(), vol 9423. Springer, Cham. https://doi.org/10.1007/978-3-319-25751-8_16
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DOI: https://doi.org/10.1007/978-3-319-25751-8_16
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