Open Access

Segmentation of Fingerprint Images Using Linear Classifier

EURASIP Journal on Advances in Signal Processing20042004:978695

DOI: 10.1155/S1110865704309194

Received: 28 October 2002

Published: 21 April 2004


An algorithm for the segmentation of fingerprints and a criterion for evaluating the block feature are presented. The segmentation uses three block features: the block clusters degree, the block mean information, and the block variance. An optimal linear classifier has been trained for the classification per block and the criteria of minimal number of misclassified samples are used. Morphology has been applied as postprocessing to reduce the number of classification errors. The algorithm is tested on FVC2002 database, only 2.45% of the blocks are misclassified, while the postprocessing further reduces this ratio. Experiments have shown that the proposed segmentation method performs very well in rejecting false fingerprint features from the noisy background.

Keywords and phrases

fingerprint image segmentation block features linear classification image processing

Authors’ Affiliations

Intelligent Bioinformatics Systems Division, Institute of Automation, The Chinese Academy of Sciences


© Chen et al. 2004