A combination fingerprint classifier

Author

Senior, Andrew

Author_Institution

IBM Thomas J. Watson Res. Center, Yorktown Heights, NY, USA

Volume

23

Issue

10

fYear

2001

fDate

10/1/2001 12:00:00 AM

Firstpage

1165

Lastpage

1174

Abstract

Fingerprint classification is an important indexing method for any large scale fingerprint recognition system or database as a method for reducing the number of fingerprints that need to be searched when looking for a matching print. Fingerprints are generally classified into broad categories based on global characteristics. This paper describes novel methods of classification using hidden Markov models and decision trees to recognize the ridge structure of the print, without needing to detect singular points. The methods are compared and combined with a standard fingerprint classification algorithm and results for the combination are presented using a standard database of fingerprint images. The paper also describes a method for achieving any level of accuracy required of the system by sacrificing the efficiency of the classifier. The accuracy of the combination classifier is shown to be higher than that of the two state-of-the-art systems tested under the same conditions

Keywords

decision trees; feature extraction; fingerprint identification; hidden Markov models; neural nets; pattern classification; Henry fingerprint classification; NIST database; decision trees; feature extraction; hidden Markov models; neural networks; pattern classification; Classification algorithms; Classification tree analysis; Decision trees; Fingerprint recognition; Hidden Markov models; Image databases; Image matching; Indexing; Large-scale systems; System testing;

fLanguage

English

Journal_Title

Pattern Analysis and Machine Intelligence, IEEE Transactions on

Publisher

ieee

ISSN

0162-8828

Type

jour

DOI

10.1109/34.954606

Filename

954606