11-08-2012, 02:02 PM
Fingerprint Identification
[attachment=31114]
Introduction
Factors in favor of fingerprint applications:
small and inexpensive capture devices (about 100 USD);
fast computing hardware;
recognition rate meets the needs of many applications (about 1 sec);
increasing number of networks and Internet transactions;
awareness of the need for ease-of-use as an important component of reliable security
well accepted by public
Feature Types
The lines that flow in various patterns across fingerprints are called ridges and the space between ridges are valleys.
Fingerprint features (associated with some matching algorithm):
ridge pattern - global pattern matching;
minutiae (ridge ending and ridge bifurcation) - minutiae matching; - attributes: type, (x,y)- location, orientation
Image Enhancement
Noise in the fingerprint image is due to:
dry or wet skin, dirt, cut, worn, noise of the capture device.
Two image enhancement operations:
(i) the adaptive matched filter (enhances ridges oriented in the same direction as those I in the same locality) ;
(ii) adaptive thresholding (binarization: im2bw; graythresh).
Estimation of orientation field (gradient method, slit-sums, etc.).
Local adaptive thresholding can be used (images with different contrast).
Other image enhancement methods
Image can be divided into windows. Local ridge orientation is found for
each window.
Spatial or frequency domain processing.
D. Maio and D. Maltoni proposed an algorithm that traces ridges and
detect minutiae using grayscale image.
Multi-resolution approach (multiple window sizes).
[attachment=31114]
Introduction
Factors in favor of fingerprint applications:
small and inexpensive capture devices (about 100 USD);
fast computing hardware;
recognition rate meets the needs of many applications (about 1 sec);
increasing number of networks and Internet transactions;
awareness of the need for ease-of-use as an important component of reliable security
well accepted by public
Feature Types
The lines that flow in various patterns across fingerprints are called ridges and the space between ridges are valleys.
Fingerprint features (associated with some matching algorithm):
ridge pattern - global pattern matching;
minutiae (ridge ending and ridge bifurcation) - minutiae matching; - attributes: type, (x,y)- location, orientation
Image Enhancement
Noise in the fingerprint image is due to:
dry or wet skin, dirt, cut, worn, noise of the capture device.
Two image enhancement operations:
(i) the adaptive matched filter (enhances ridges oriented in the same direction as those I in the same locality) ;
(ii) adaptive thresholding (binarization: im2bw; graythresh).
Estimation of orientation field (gradient method, slit-sums, etc.).
Local adaptive thresholding can be used (images with different contrast).
Other image enhancement methods
Image can be divided into windows. Local ridge orientation is found for
each window.
Spatial or frequency domain processing.
D. Maio and D. Maltoni proposed an algorithm that traces ridges and
detect minutiae using grayscale image.
Multi-resolution approach (multiple window sizes).