Based on SVM Automatic Measures of Fingerprint Image Quality

This paper presents a novel method for fingerprint image quality. Five features are extracted from the fingerprint image to analyze the quality and the feature vector is formed from the five features. Then SVM classifier, which can solve small-sample learning problems with good generalization, is tr...

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Bibliographic Details
Published in2008 IEEE Pacific-Asia Workshop on Computational Intelligence and Industrial Application Vol. 1; pp. 575 - 578
Main Authors Lianhua Liu, Taizhe Tan, Yinwei Zhan
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.12.2008
Subjects
Conferences
Feature extraction
Fingerprint recognition
Gabor filters
Image databases
Image matching
Image quality
Spatial databases
Support vector machine classification
Support vector machines
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Summary:This paper presents a novel method for fingerprint image quality. Five features are extracted from the fingerprint image to analyze the quality and the feature vector is formed from the five features. Then SVM classifier, which can solve small-sample learning problems with good generalization, is trained to classify the fingerprint image. The fingerprint image is separated into one of the three classes, good-quality,medium-quality, or poor-quality. Experimental results on FVC2004 and a private database show that the proposed method is an effective and efficient scheme to measure the quality of the fingerprint image. Our method overcomes the shortcoming that most of existing methods have,considering the correlation of each quality feature as linear.
ISBN:0769534902
9780769534909
DOI:10.1109/PACIIA.2008.108