Performance improvement in a fingerprint classification system using anisotropic diffusion

Gonzalo Vallarino; Gustavo Gianarelli; Jose Barattini; Alvaro Gómez; Alicia Fernández; Alvaro Pardo

doi:10.1007/978-3-540-30463-0_73

Performance improvement in a fingerprint classification system using anisotropic diffusion

Gonzalo Vallarino, Gustavo Gianarelli, Jose Barattini, Alvaro Gómez, Alicia Fernández, Alvaro Pardo

Universidad de la República

Research output: Chapter in Book/Report/Conference proceeding › Chapter › peer-review

5 Scopus citations

Abstract

In a previous work, [1], we evaluated a classification algorithm based on the Karu-Jain method [2] and compared the performance with a fully manual method used at the Dirección Nacional de Identification Civil (DNIC). In this paper, we analyze the high performance improvement achieved using anisotropic diffusion instead of pure averaging for the directions smoothing. We also define a quality measure that shows high correlation with the experts' criteria. The results are evaluated over 2800 images extracted from a 4 million fingerprint card archive maintained by DNIC.

Original language	English
Title of host publication	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Editors	Alberto Sanfeliu, Jose Francisco Martinez-Trinidad, Jesus Ariel Carrasco-Ochoa
Publisher	Springer Verlag
Pages	582-588
Number of pages	7
ISBN (Print)	3540235272
DOIs	https://doi.org/10.1007/978-3-540-30463-0_73
State	Published - 2004
Externally published	Yes

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	3287
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

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10.1007/978-3-540-30463-0_73

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Vallarino, G., Gianarelli, G., Barattini, J., Gómez, A., Fernández, A., & Pardo, A. (2004). Performance improvement in a fingerprint classification system using anisotropic diffusion. In A. Sanfeliu, J. F. Martinez-Trinidad, & J. A. Carrasco-Ochoa (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 582-588). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 3287). Springer Verlag. https://doi.org/10.1007/978-3-540-30463-0_73

Vallarino, Gonzalo ; Gianarelli, Gustavo ; Barattini, Jose et al. / Performance improvement in a fingerprint classification system using anisotropic diffusion. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). editor / Alberto Sanfeliu ; Jose Francisco Martinez-Trinidad ; Jesus Ariel Carrasco-Ochoa. Springer Verlag, 2004. pp. 582-588 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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author = "Gonzalo Vallarino and Gustavo Gianarelli and Jose Barattini and Alvaro G{\'o}mez and Alicia Fern{\'a}ndez and Alvaro Pardo",

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Vallarino, G, Gianarelli, G, Barattini, J, Gómez, A, Fernández, A & Pardo, A 2004, Performance improvement in a fingerprint classification system using anisotropic diffusion. in A Sanfeliu, JF Martinez-Trinidad & JA Carrasco-Ochoa (eds), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 3287, Springer Verlag, pp. 582-588. https://doi.org/10.1007/978-3-540-30463-0_73

Performance improvement in a fingerprint classification system using anisotropic diffusion. / Vallarino, Gonzalo; Gianarelli, Gustavo; Barattini, Jose et al.
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). ed. / Alberto Sanfeliu; Jose Francisco Martinez-Trinidad; Jesus Ariel Carrasco-Ochoa. Springer Verlag, 2004. p. 582-588 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 3287).

Research output: Chapter in Book/Report/Conference proceeding › Chapter › peer-review

TY - CHAP

T1 - Performance improvement in a fingerprint classification system using anisotropic diffusion

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AU - Gianarelli, Gustavo

AU - Barattini, Jose

AU - Gómez, Alvaro

AU - Fernández, Alicia

AU - Pardo, Alvaro

PY - 2004

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AB - In a previous work, [1], we evaluated a classification algorithm based on the Karu-Jain method [2] and compared the performance with a fully manual method used at the Dirección Nacional de Identification Civil (DNIC). In this paper, we analyze the high performance improvement achieved using anisotropic diffusion instead of pure averaging for the directions smoothing. We also define a quality measure that shows high correlation with the experts' criteria. The results are evaluated over 2800 images extracted from a 4 million fingerprint card archive maintained by DNIC.

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DO - 10.1007/978-3-540-30463-0_73

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T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

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BT - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

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Vallarino G, Gianarelli G, Barattini J, Gómez A, Fernández A, Pardo A. Performance improvement in a fingerprint classification system using anisotropic diffusion. In Sanfeliu A, Martinez-Trinidad JF, Carrasco-Ochoa JA, editors, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Springer Verlag. 2004. p. 582-588. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-540-30463-0_73

Performance improvement in a fingerprint classification system using anisotropic diffusion

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