A Multi-Classifier Approach to Fingerprint Classification View Full Text


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Article Info

DATE

2002-06

AUTHORS

Raffaele Cappelli, Dario Maio, Davide Maltoni

ABSTRACT

: Fingerprint classification is a challenging pattern recognition problem which plays a fundamental role in most of the large fingerprint-based identification systems. Due to the intrinsic class ambiguity and the difficulty of processing very low quality images (which constitute a significant proportion), automatic fingerprint classification performance is currently below operating requirements, and most of the classification work is still carried out manually or semi-automatically. This paper explores the advantages of combining the MASKS and MKL-based classifiers, which we have specifically designed for the fingerprint classification task. In particular, a combination at the ‘abstract level’ is proposed for exclusive classification, whereas a fusion at the ‘measurement level’ is introduced for continuous classification. The advantages of coupling these distinct techniques are well evident; in particular, in the case of exclusive classification, the FBI challenge, requiring a classification error ≤ 1% at 20% rejection, was met on NIST-DB14. More... »

PAGES

136-144

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s100440200012

DOI

http://dx.doi.org/10.1007/s100440200012

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1035234412


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