Learning partitioned least squares filters for fingerprintenhancement
Ghosal, S.
Udupa, R.
Panknti, S.
Ratha, N.K.
IBM India Res. Lab., New Delhi;
This paper appears in: Applications of Computer Vision, 2000, Fifth IEEE Workshop on.
Publication Date: 2000
On page(s): 2-7
Meeting Date: 12/04/2000 - 12/06/2000
Location: Palm Springs, CA, USA
ISBN: 0-7695-0813-8
References Cited: 6
INSPEC Accession Number: 6806428
Digital Object Identifier: 10.1109/WACV.2000.895395
Current Version Published: 2002-08-06
Abstract
Fingerprint images contain varying amount of noise because of the
limitations of the fingerprint acquisition process. It is often
necessary to enhance such noisy fingerprint images so that the features
extracted from them are reliable. We propose a novel approach to
fingerprint enhancement where a set of filters are learned using the
“learn-from-example” paradigm. An expert provides the ground
truth information for ridges in a small set of representative
fingerprint images. The space of local fingerprint patterns in a small
neighborhood is partitioned into a set of expressive yet computationally
simple classes. A filter is learnt for each partition by finding the
optimal linear mapping (in least-square sense) from the input to the
enhanced space. The proposed approach offers distinct performance and
speed advantages for a wide variety of fingerprint images
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