Pacific-Asia Workshop on Computational Intelligence and Industrial Application, IEEE (2008)
Dec. 19, 2008 to Dec. 20, 2008
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.
L. Liu, T. Tan and Y. Zhan, "Based on SVM Automatic Measures of Fingerprint Image Quality," 2008 Pacific-Asia Workshop on Computational Intelligence and Industrial Application. PACIIA 2008(PACIIA), Wuhan, 2008, pp. 575-578.