Multiple decision templates with adaptive features for fingerprint classification

Jun Ki Min; Sung Bae Cho

doi:10.1142/S0218001407006009

Multiple decision templates with adaptive features for fingerprint classification

Jun Ki Min, Sung Bae Cho

Department of Computer Science

Research output: Contribution to journal › Article › peer-review

6 Citations (Scopus)

Abstract

This paper proposes a novel fingerprint classification method using multiple decision templates of Support Vector Machines (SVMs) with adaptive features. In order to overcome intra-class and inter-class ambiguities of fingerprints, the proposed method extracts a feature vector from an adaptively detected feature region and classifies the feature vector using SVMs. The outputs of the SVMs are then combined by multiple decision templates that make several per class. Experimental results on NIST4 fingerprint database revealed the effectiveness and validity of the proposed method for fingerprint classification.

Original language	English
Pages (from-to)	1323-1338
Number of pages	16
Journal	International Journal of Pattern Recognition and Artificial Intelligence
Volume	21
Issue number	8
DOIs	https://doi.org/10.1142/S0218001407006009
Publication status	Published - 2007 Dec

Bibliographical note

Funding Information:
This work was supported by the Korea Science and Engineering Foundation (KOSEF) through the Biometrics Engineering Research Center (BERC) at Yonsei University.

All Science Journal Classification (ASJC) codes

Software
Computer Vision and Pattern Recognition
Artificial Intelligence

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10.1142/S0218001407006009

Cite this

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title = "Multiple decision templates with adaptive features for fingerprint classification",

abstract = "This paper proposes a novel fingerprint classification method using multiple decision templates of Support Vector Machines (SVMs) with adaptive features. In order to overcome intra-class and inter-class ambiguities of fingerprints, the proposed method extracts a feature vector from an adaptively detected feature region and classifies the feature vector using SVMs. The outputs of the SVMs are then combined by multiple decision templates that make several per class. Experimental results on NIST4 fingerprint database revealed the effectiveness and validity of the proposed method for fingerprint classification.",

author = "Min, {Jun Ki} and Cho, {Sung Bae}",

note = "Funding Information: This work was supported by the Korea Science and Engineering Foundation (KOSEF) through the Biometrics Engineering Research Center (BERC) at Yonsei University.",

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AU - Cho, Sung Bae

N1 - Funding Information: This work was supported by the Korea Science and Engineering Foundation (KOSEF) through the Biometrics Engineering Research Center (BERC) at Yonsei University.

PY - 2007/12

Y1 - 2007/12

N2 - This paper proposes a novel fingerprint classification method using multiple decision templates of Support Vector Machines (SVMs) with adaptive features. In order to overcome intra-class and inter-class ambiguities of fingerprints, the proposed method extracts a feature vector from an adaptively detected feature region and classifies the feature vector using SVMs. The outputs of the SVMs are then combined by multiple decision templates that make several per class. Experimental results on NIST4 fingerprint database revealed the effectiveness and validity of the proposed method for fingerprint classification.

AB - This paper proposes a novel fingerprint classification method using multiple decision templates of Support Vector Machines (SVMs) with adaptive features. In order to overcome intra-class and inter-class ambiguities of fingerprints, the proposed method extracts a feature vector from an adaptively detected feature region and classifies the feature vector using SVMs. The outputs of the SVMs are then combined by multiple decision templates that make several per class. Experimental results on NIST4 fingerprint database revealed the effectiveness and validity of the proposed method for fingerprint classification.

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Multiple decision templates with adaptive features for fingerprint classification

Abstract

Bibliographical note

All Science Journal Classification (ASJC) codes

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