27-06-2012, 03:59 PM
Fingerprint Identification
[attachment=25794]
I. Introduction
Fingerprints are imprints formed by friction ridges of the skin and thumbs. They
have long been used for identification because of their immutability and individuality.
Immutability refers to the permanent and unchanging character of the pattern on each
finger. Individuality refers to the uniqueness of ridge details across individuals; the
probability that two fingerprints are alike is about 1 in 1.9x1015.
However, manual fingerprint verification is so tedious, time consuming and expensive that
is incapable of meeting today’s increasing performance requirements. An automatic
fingerprint identification system is widely adopted in many applications such as building or
area security and ATM machines [1-2].
Two approaches will be described in this project for fingerprint recognition:
• Approach 1: Based on minutiae located in a fingerprint
• Approach 2: Based on frequency content and ridge orientation of a fingerprint
II. First Approach
Most automatic systems for fingerprint comparison are based on minutiae matching
Minutiae are local discontinuities in the fingerprint pattern. A total of 150 different
minutiae types have been identified. In practice only ridge ending and ridge bifurcation
minutiae types are used in fingerprint recognition. Examples of minutiae are shown in
figure 1.
(a) (b)
Figure 1. (a) Different minutiae types, (b) Ridge ending & Bifurcation
Many known algorithms have been developed for minutiae extraction based on orientation
and gradients of the orientation fields of the ridges [3]. In this project we will adopt the
method used by Leung where minutiae are extracted using feedforward artificial neural
networks [1].
The building blocks of a fingerprint recognition system are:
Figure 2. Fingerprint recognition system
a) Image Acquisition
A number of methods are used to acquire fingerprints. Among them, the inked
impression method remains the most popular one. Inkless fingerprint scanners are also
present eliminating the intermediate digitization process.
In our project we will use the database available for free at University of Bologna
(http://bias.csr.unibo.it/fvc2000/) as well as building an AUB database; each one must
gather 36 inked fingerprints images from 3 persons (12 images per finger).
Fingerprint quality is very important since it affects directly the minutiae extraction
algorithm. Two types of degradation usually affect fingerprint images: 1) the ridge lines are
not strictly continuous since they sometimes include small breaks (gaps); 2) parallel ridge
lines are not always well separated due to the presence of cluttering noise. The resolution
of the scanned fingerprints must be 500 dpi while the size is 300x300.
b) Edge Detection
An edge is the boundary between two regions with relatively distinct gray level
properties. The idea underlying most edge-detection techniques is on the computation of a
local derivative operator such as ‘Roberts’, ‘Prewitt’ or ‘Sobel’ operators.
In practice, the set of pixels obtained from the edge detection algorithm seldom
characterizes a boundary completely because of noise, breaks in the boundary and other
effects that introduce spurious intensity discontinuities. Thus, edge detection algorithms
typically are followed by linking and other boundary detection procedures designed to
assemble edge pixels into meaningful boundaries.
For a detailed explanation refer to “Digital Image Processing” by Gonzalez, chapters 3 - 4.
It is also useful to check the Image Toolbox Demos available in MATLAB.
c) Thinning
An important approach to representing the structural shape of a plane region is to
reduce it to a graph. This reduction may be accomplished by obtaining the skeleton of the
region via thinning (also called skeletonizing) algorithm.
The thinning algorithm while deleting unwanted edge points should not:
• Remove end points.
• Break connectedness
Image
Acquisition
Edge
Detection
Thinning Feature
Extractor
Classifier
Physical
Fingerprint
Classification
Decision
• Cause excessive erosion of the region
For a detailed explanation refer to “Digital Image Processing” by Gonzalez, chapter 9. It is
also useful to check the following link:
http://www.fmrib.ox.ac.uk/~steve/susan/t...node2.html
d) Feature Extraction
Extraction of appropriate features is one of the most important tasks for a
recognition system. The feature extraction method used in [1] will be explained below.
A multilayer perceptron (MLP) of three layers is trained to detect the minutiae in the
thinned fingerprint image of size 300x300. The first layer of the network has nine neurons
associated with the components of the input vector. The hidden layer has five neurons and
the output layer has one neuron. The network is trained to output a “1” when the input
window in centered on a minutiae and a “0” when it is not. Figure 3 shows the initial
training patterns which are composed of 16 samples of bifurcations in eight different
orientations and 36 samples of non-bifurcations. The networking will be trained using:
• The backpropagation algorithm with momentum and learning rate of 0.3.
• The Al-Alaoui backpropagation algorithm.
State the number of epochs needed for convergence as well as the training time for the two
methods. Once the network is trained, the next step is to input the prototype fingerprint
images to extract the minutiae. The fingerprint image is scanned using a 3x3 window given
[attachment=25794]
I. Introduction
Fingerprints are imprints formed by friction ridges of the skin and thumbs. They
have long been used for identification because of their immutability and individuality.
Immutability refers to the permanent and unchanging character of the pattern on each
finger. Individuality refers to the uniqueness of ridge details across individuals; the
probability that two fingerprints are alike is about 1 in 1.9x1015.
However, manual fingerprint verification is so tedious, time consuming and expensive that
is incapable of meeting today’s increasing performance requirements. An automatic
fingerprint identification system is widely adopted in many applications such as building or
area security and ATM machines [1-2].
Two approaches will be described in this project for fingerprint recognition:
• Approach 1: Based on minutiae located in a fingerprint
• Approach 2: Based on frequency content and ridge orientation of a fingerprint
II. First Approach
Most automatic systems for fingerprint comparison are based on minutiae matching
Minutiae are local discontinuities in the fingerprint pattern. A total of 150 different
minutiae types have been identified. In practice only ridge ending and ridge bifurcation
minutiae types are used in fingerprint recognition. Examples of minutiae are shown in
figure 1.
(a) (b)
Figure 1. (a) Different minutiae types, (b) Ridge ending & Bifurcation
Many known algorithms have been developed for minutiae extraction based on orientation
and gradients of the orientation fields of the ridges [3]. In this project we will adopt the
method used by Leung where minutiae are extracted using feedforward artificial neural
networks [1].
The building blocks of a fingerprint recognition system are:
Figure 2. Fingerprint recognition system
a) Image Acquisition
A number of methods are used to acquire fingerprints. Among them, the inked
impression method remains the most popular one. Inkless fingerprint scanners are also
present eliminating the intermediate digitization process.
In our project we will use the database available for free at University of Bologna
(http://bias.csr.unibo.it/fvc2000/) as well as building an AUB database; each one must
gather 36 inked fingerprints images from 3 persons (12 images per finger).
Fingerprint quality is very important since it affects directly the minutiae extraction
algorithm. Two types of degradation usually affect fingerprint images: 1) the ridge lines are
not strictly continuous since they sometimes include small breaks (gaps); 2) parallel ridge
lines are not always well separated due to the presence of cluttering noise. The resolution
of the scanned fingerprints must be 500 dpi while the size is 300x300.
b) Edge Detection
An edge is the boundary between two regions with relatively distinct gray level
properties. The idea underlying most edge-detection techniques is on the computation of a
local derivative operator such as ‘Roberts’, ‘Prewitt’ or ‘Sobel’ operators.
In practice, the set of pixels obtained from the edge detection algorithm seldom
characterizes a boundary completely because of noise, breaks in the boundary and other
effects that introduce spurious intensity discontinuities. Thus, edge detection algorithms
typically are followed by linking and other boundary detection procedures designed to
assemble edge pixels into meaningful boundaries.
For a detailed explanation refer to “Digital Image Processing” by Gonzalez, chapters 3 - 4.
It is also useful to check the Image Toolbox Demos available in MATLAB.
c) Thinning
An important approach to representing the structural shape of a plane region is to
reduce it to a graph. This reduction may be accomplished by obtaining the skeleton of the
region via thinning (also called skeletonizing) algorithm.
The thinning algorithm while deleting unwanted edge points should not:
• Remove end points.
• Break connectedness
Image
Acquisition
Edge
Detection
Thinning Feature
Extractor
Classifier
Physical
Fingerprint
Classification
Decision
• Cause excessive erosion of the region
For a detailed explanation refer to “Digital Image Processing” by Gonzalez, chapter 9. It is
also useful to check the following link:
http://www.fmrib.ox.ac.uk/~steve/susan/t...node2.html
d) Feature Extraction
Extraction of appropriate features is one of the most important tasks for a
recognition system. The feature extraction method used in [1] will be explained below.
A multilayer perceptron (MLP) of three layers is trained to detect the minutiae in the
thinned fingerprint image of size 300x300. The first layer of the network has nine neurons
associated with the components of the input vector. The hidden layer has five neurons and
the output layer has one neuron. The network is trained to output a “1” when the input
window in centered on a minutiae and a “0” when it is not. Figure 3 shows the initial
training patterns which are composed of 16 samples of bifurcations in eight different
orientations and 36 samples of non-bifurcations. The networking will be trained using:
• The backpropagation algorithm with momentum and learning rate of 0.3.
• The Al-Alaoui backpropagation algorithm.
State the number of epochs needed for convergence as well as the training time for the two
methods. Once the network is trained, the next step is to input the prototype fingerprint
images to extract the minutiae. The fingerprint image is scanned using a 3x3 window given