Fingerprint matching algorithm using shape context and orientation descriptors
04 Nov 2010
08 Aug 2011)
Fingerprint matching code using a hybrid descriptor. EER < 1% (approx. 0.75%) on FVC2002 Db1_A.
|ridgesegment(im, blksze, thresh)
% RIDGESEGMENT - Normalises fingerprint image and segments ridge region
% Function identifies ridge regions of a fingerprint image and returns a
% mask identifying this region. It also normalises the intesity values of
% the image so that the ridge regions have zero mean, unit standard
% This function breaks the image up into blocks of size blksze x blksze and
% evaluates the standard deviation in each region. If the standard
% deviation is above the threshold it is deemed part of the fingerprint.
% Note that the image is normalised to have zero mean, unit standard
% deviation prior to performing this process so that the threshold you
% specify is relative to a unit standard deviation.
% Usage: [normim, mask, maskind] = ridgesegment(im, blksze, thresh)
% Arguments: im - Fingerprint image to be segmented.
% blksze - Block size over which the the standard
% deviation is determined (try a value of 16).
% thresh - Threshold of standard deviation to decide if a
% block is a ridge region (Try a value 0.1 - 0.2)
% Returns: normim - Image where the ridge regions are renormalised to
% have zero mean, unit standard deviation.
% mask - Mask indicating ridge-like regions of the image,
% 0 for non ridge regions, 1 for ridge regions.
% maskind - Vector of indices of locations within the mask.
% Suggested values for a 500dpi fingerprint image:
% [normim, mask, maskind] = ridgesegment(im, 16, 0.1)
% See also: RIDGEORIENT, RIDGEFREQ, RIDGEFILTER
% Peter Kovesi
% School of Computer Science & Software Engineering
% The University of Western Australia
% pk at csse uwa edu au
% January 2005
function [normim, mask, maskind] = ridgesegment(im, blksze, thresh)
im = normalise(im,0,1); % normalise to have zero mean, unit std dev
fun = inline('std(x(:))*ones(size(x))');
stddevim = blkproc(im, [blksze blksze], fun);
mask = stddevim > thresh;
maskind = find(mask);
% Renormalise image so that the *ridge regions* have zero mean, unit
% standard deviation.
im = im - mean(im(maskind));
normim = im/std(im(maskind));