Fingerprint classification and matching are two key issues in automatic fingerprint recognition. Generally, fingerprint recognition is based on a set of relevant local characteristics, such as ridge ending and bifurcation (minutiae). Fingerprint classification is based on fingerprint global features, such as core and delta singularity points. Unfortunately, singularity points are not always present in a fingerprint image: the acquisition process is not ideal, so that the fingerprint is broken, or the fingerprint belongs to the arch class. In the above cases, pseudo-singularity-points will be detected and extracted to make possible fingerprint classification and matching. As result, fingerprint processing involves few steps and, in the same way, fingerprint matching involves the comparison of few features with recognition rates comparable to the standard minutiae based systems. The experiments trials have been conducted on many official Fingerprint Verification Competition (FVC) databases. The achieved results show the effectiveness of the proposed approach, obtaining a False Acceptance Rate (FAR) = 1.22% and a False Rejection Rate (FRR) = 9.23% with FVC2002 DB2-A database. In the best of case, a FAR=0.26% and a FRR=7.36% with FVC2000 DB1-B database is achieved. To the best of our knowledge, this is the first recognition system based only on singularity regions.

Conti, V., Militello, C., Vitabile, S., Sorbello, F. (2010). Introducing Pseudo-Singularity Points for Efficient Fingerprints Classification and Recognition. In Proceedings of 4th IEEE International Conference on Complex, Intelligent and Software Intensive Systems (CISIS 2010) (pp.368-375) [10.1109/CISIS.2010.134].

Introducing Pseudo-Singularity Points for Efficient Fingerprints Classification and Recognition

MILITELLO, Carmelo;VITABILE, Salvatore;SORBELLO, Filippo
2010-01-01

Abstract

Fingerprint classification and matching are two key issues in automatic fingerprint recognition. Generally, fingerprint recognition is based on a set of relevant local characteristics, such as ridge ending and bifurcation (minutiae). Fingerprint classification is based on fingerprint global features, such as core and delta singularity points. Unfortunately, singularity points are not always present in a fingerprint image: the acquisition process is not ideal, so that the fingerprint is broken, or the fingerprint belongs to the arch class. In the above cases, pseudo-singularity-points will be detected and extracted to make possible fingerprint classification and matching. As result, fingerprint processing involves few steps and, in the same way, fingerprint matching involves the comparison of few features with recognition rates comparable to the standard minutiae based systems. The experiments trials have been conducted on many official Fingerprint Verification Competition (FVC) databases. The achieved results show the effectiveness of the proposed approach, obtaining a False Acceptance Rate (FAR) = 1.22% and a False Rejection Rate (FRR) = 9.23% with FVC2002 DB2-A database. In the best of case, a FAR=0.26% and a FRR=7.36% with FVC2000 DB1-B database is achieved. To the best of our knowledge, this is the first recognition system based only on singularity regions.
2010
4th IEEE International Conference on Complex, Intelligent and Software Intensive Systems (CISIS 2010)
Andrzej Frycz Modrzewski Cracow College, Krakow, Poland
February, 15th - 18th 2010
4th
2010
8
Conti, V., Militello, C., Vitabile, S., Sorbello, F. (2010). Introducing Pseudo-Singularity Points for Efficient Fingerprints Classification and Recognition. In Proceedings of 4th IEEE International Conference on Complex, Intelligent and Software Intensive Systems (CISIS 2010) (pp.368-375) [10.1109/CISIS.2010.134].
Proceedings (atti dei congressi)
Conti, V; Militello, C; Vitabile, S; Sorbello, F
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10447/57100
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