Fusion in Fingerprint Authentication: Two Finger Scenarios

This paper presents our initial results of fingerprint fusion by analysing all pair combinations of ten fingers (i.e. 45 pairs). As a fingerprint verification algorithm we use a commercially available software package from Neurotechnology. As a test data set we use GUC100 multi scanner fingerprint d...

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Published in2010 Sixth International Conference on Intelligent Information Hiding and Multimedia Signal Processing pp. 356 - 359
Main Authors Gafurov, D, Busch, C, Bours, P, Bian Yang
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.10.2010
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ISBN1424483786
9781424483785
DOI10.1109/IIHMSP.2010.93

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Abstract This paper presents our initial results of fingerprint fusion by analysing all pair combinations of ten fingers (i.e. 45 pairs). As a fingerprint verification algorithm we use a commercially available software package from Neurotechnology. As a test data set we use GUC100 multi scanner fingerprint database which contains fingerprint images of all ten fingers from 100 subjects collected over several months. We conduct score level fusion and apply two fusion methods, namely max and sum rules. Our analysis indicates that fusion using both rules provide improvement in all scenarios, and max rule showing slightly better performance compared to max rule's performance. The obtained performance improvements (in terms of EER) for sum and max rules are in the range of 60.2%-91.1% and 67.4%-93.5%, respectively.
AbstractList This paper presents our initial results of fingerprint fusion by analysing all pair combinations of ten fingers (i.e. 45 pairs). As a fingerprint verification algorithm we use a commercially available software package from Neurotechnology. As a test data set we use GUC100 multi scanner fingerprint database which contains fingerprint images of all ten fingers from 100 subjects collected over several months. We conduct score level fusion and apply two fusion methods, namely max and sum rules. Our analysis indicates that fusion using both rules provide improvement in all scenarios, and max rule showing slightly better performance compared to max rule's performance. The obtained performance improvements (in terms of EER) for sum and max rules are in the range of 60.2%-91.1% and 67.4%-93.5%, respectively.
Author Bours, P
Gafurov, D
Bian Yang
Busch, C
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  organization: Norwegian Inf. Security Lab. (NISLab), Norway
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Snippet This paper presents our initial results of fingerprint fusion by analysing all pair combinations of ten fingers (i.e. 45 pairs). As a fingerprint verification...
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StartPage 356
SubjectTerms Fingerprint recognition
Fingers
Indexes
ISO standards
Magnetic resonance
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Title Fusion in Fingerprint Authentication: Two Finger Scenarios
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