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Improving fingerprint verification using minutiae triplets.

Medina-Pérez MA, García-Borroto M, Gutierrez-Rodríguez AE, Altamirano-Robles L - Sensors (Basel) (2012)

Bottom Line: Algorithms based on minutia triplets, an important matcher family, present some drawbacks that impact their accuracy, such as dependency to the order of minutiae in the feature, insensitivity to the reflection of minutiae triplets, and insensitivity to the directions of the minutiae relative to the sides of the triangle.To make M3gl faster, it includes some optimizations to discard non-matching minutia triplets without comparing the whole representation.In comparison with six verification algorithms, M3gl achieves the highest accuracy in the lowest matching time, using FVC2002 and FVC2004 databases.

View Article: PubMed Central - PubMed

Affiliation: Centro de Bioplantas, Universidad de Ciego de Ávila, Ciego de Ávila, Cuba. migue@bioplantas.cu

ABSTRACT
Improving fingerprint matching algorithms is an active and important research area in fingerprint recognition. Algorithms based on minutia triplets, an important matcher family, present some drawbacks that impact their accuracy, such as dependency to the order of minutiae in the feature, insensitivity to the reflection of minutiae triplets, and insensitivity to the directions of the minutiae relative to the sides of the triangle. To alleviate these drawbacks, we introduce in this paper a novel fingerprint matching algorithm, named M3gl. This algorithm contains three components: a new feature representation containing clockwise-arranged minutiae without a central minutia, a new similarity measure that shifts the triplets to find the best minutiae correspondence, and a global matching procedure that selects the alignment by maximizing the amount of global matching minutiae. To make M3gl faster, it includes some optimizations to discard non-matching minutia triplets without comparing the whole representation. In comparison with six verification algorithms, M3gl achieves the highest accuracy in the lowest matching time, using FVC2002 and FVC2004 databases.

No MeSH data available.


Two examples where all matching algorithms fail but our algorithm finds true matching minutiae. The first row contains fingerprints db1_36_1 and db1_36_4 of database DB1_A (FVC2002); the second row contains fingerprints 85_6 and 85_8 of database DB1_A (FVC2004).
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f7-sensors-12-03418: Two examples where all matching algorithms fail but our algorithm finds true matching minutiae. The first row contains fingerprints db1_36_1 and db1_36_4 of database DB1_A (FVC2002); the second row contains fingerprints 85_6 and 85_8 of database DB1_A (FVC2004).

Mentions: Figures 5 and 6 show that M3gl achieves lower FNMR for most of the FMR values. Tables 2 and 3 show that our algorithm achieves the best results for most of the performance indicators. Figure 7 shows examples where our algorithm is able to find true matching minutiae in difficult cases (partial fingerprints with low overlapping, non-linear distortion and low quality) where the other algorithms fail. M3gl is also the fastest algorithm due to the following reasons:


Improving fingerprint verification using minutiae triplets.

Medina-Pérez MA, García-Borroto M, Gutierrez-Rodríguez AE, Altamirano-Robles L - Sensors (Basel) (2012)

Two examples where all matching algorithms fail but our algorithm finds true matching minutiae. The first row contains fingerprints db1_36_1 and db1_36_4 of database DB1_A (FVC2002); the second row contains fingerprints 85_6 and 85_8 of database DB1_A (FVC2004).
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3376607&req=5

f7-sensors-12-03418: Two examples where all matching algorithms fail but our algorithm finds true matching minutiae. The first row contains fingerprints db1_36_1 and db1_36_4 of database DB1_A (FVC2002); the second row contains fingerprints 85_6 and 85_8 of database DB1_A (FVC2004).
Mentions: Figures 5 and 6 show that M3gl achieves lower FNMR for most of the FMR values. Tables 2 and 3 show that our algorithm achieves the best results for most of the performance indicators. Figure 7 shows examples where our algorithm is able to find true matching minutiae in difficult cases (partial fingerprints with low overlapping, non-linear distortion and low quality) where the other algorithms fail. M3gl is also the fastest algorithm due to the following reasons:

Bottom Line: Algorithms based on minutia triplets, an important matcher family, present some drawbacks that impact their accuracy, such as dependency to the order of minutiae in the feature, insensitivity to the reflection of minutiae triplets, and insensitivity to the directions of the minutiae relative to the sides of the triangle.To make M3gl faster, it includes some optimizations to discard non-matching minutia triplets without comparing the whole representation.In comparison with six verification algorithms, M3gl achieves the highest accuracy in the lowest matching time, using FVC2002 and FVC2004 databases.

View Article: PubMed Central - PubMed

Affiliation: Centro de Bioplantas, Universidad de Ciego de Ávila, Ciego de Ávila, Cuba. migue@bioplantas.cu

ABSTRACT
Improving fingerprint matching algorithms is an active and important research area in fingerprint recognition. Algorithms based on minutia triplets, an important matcher family, present some drawbacks that impact their accuracy, such as dependency to the order of minutiae in the feature, insensitivity to the reflection of minutiae triplets, and insensitivity to the directions of the minutiae relative to the sides of the triangle. To alleviate these drawbacks, we introduce in this paper a novel fingerprint matching algorithm, named M3gl. This algorithm contains three components: a new feature representation containing clockwise-arranged minutiae without a central minutia, a new similarity measure that shifts the triplets to find the best minutiae correspondence, and a global matching procedure that selects the alignment by maximizing the amount of global matching minutiae. To make M3gl faster, it includes some optimizations to discard non-matching minutia triplets without comparing the whole representation. In comparison with six verification algorithms, M3gl achieves the highest accuracy in the lowest matching time, using FVC2002 and FVC2004 databases.

No MeSH data available.