EURASIP Journal on Applied Signal Processing
Volume 2004 (2004), Issue 4, Pages 480-494
doi:10.1155/S1110865704309194
Abstract
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.