The Optimization of Fingerprint Parameters with
Genetic Algorithms
James Mark Kelly
CPSC-6126
Columbus State University
November 20, 2009
Abstract— An accurate Access Control System is critical in a
wide range of application domains such as national ID cards,
electronic commerce systems, and automated banking systems. A
Biometrics based Access Control System based on a person’s
physiological or behavioral characteristics is inherently more
reliable and more capable in differentiating between an
authorized person and a fraudulent imposter than traditional
methods. The objective is to design a fingerprint-based biometric
system which is capable of achieving a fully automatic “positive
personal identification” with a high level of confidence. In the
paper I’ve chosen, the authors suggest an optimization approach
for fingerprint authentication using genetic algorithms to select
the parameters used for fingerprint matching. Their application
was planned so that it could be used without a great deal of effort
for different biometric systems. Instead of estimating the
required parameters, which is what most methods do, in this
process the parameters are determined with the help of genetic
algorithms. Their test database consisted of 1200 fingerprints of
12 persons. For the confirmation of the results, the databases of
the Fingerprint Verification Contests of the years 2000, 2002, and
2004 were examined in addition.
In the best case an
improvement in the recognition performance of 38% was
observed.
Keywords; parameters; Biometrics; Equal Error Rate (EER);
BioGINA; minutiae; genetic algorith; optimization
I.
INTRODUCTION
Traditionally, users are authenticated by way of secret
knowledge or personal possession. These traditional methods
have several built-in disadvantages. Both of these traditional
methods offer the possibility of someone handing over their
authentication object to an unauthorized person, having their
authentication object stolen, or even losing the authentication
object. Also, there is really no way to make sure that the
person who has the object is the legal owner. By using
biometric systems, the person themselves are authenticated by
using typical physiological and/or behavioral characteristics of
the human being themselves. One of the disadvantages of
biometric systems is that there are not two identical samples of
a single biometric of the same person. Because of this, many
tests are run to determine the correct parameters to test in
order to achieve the desired security of a biometric
application. These parameters are estimated in many systems.
The systems that use estimated parameters have around a 2.3%
Equal Error Rate (EER) [1]. The EER is the point at which
false reject rates equal false accept rates. This paper
introduces a method which determines optimal parameters for
fingerprint recognition based on a genetic algorithm. This
results in a much lower EER and a more robust system.
Using human fingerprints for authentication purposes has
been widely used since the end of the 19 th century. They are
used because of their high acceptability, immutability and
individuality. So far, fingerprint biometric systems are the
only legally acceptable, readily automated, and mature
biometric technique [1]. A brief comparison of the nine most
popular biometric techniques is provided in Table 1 [1].
Immutability refers to the persistence of the fingerprints over
time whereas individuality is related to the uniqueness of ridge
details across individuals.
Fingerprint based identification has been one of the most
successful biometric techniques used for personal
identification. A fingerprint is the pattern of ridges and
valleys on the finger tip. A fingerprint is defined by the
uniqueness of the local ridge characteristics and their
relationships to one another. Minutiae points are these local
ridge characteristics that occur either at a ridge ending or a
ridge bifurcations as shown in figure 1 [2]. A ridge ending is
defined as the point where the ridge ends abruptly and ridge
bifurcation is the point where the ridge splits into two or more
branches.
Table 1-- Comparison of Biometric Technologies
Figure 1- Ridge ending & Bifurcation [2]
In current biometrics there are several approaches for
optimizing recognition results, which are based on the
supervised data acquisition or the combining of sever
biometric systems or algorithms. Some of the current
processes are sorting out low quality fingerprint images,
habituating users, or combining two fingers or two
representations of the same finger. Combining several
biometric modalities for performance improvement is also
possible.
Optimizations using “evolutionary” approaches can be
found in various technical systems today. The segmentation
of medical brain images or the optimizations of aerodynamic
shapes for supersonic airplanes are just two of the countless
examples for problems that are being solved by using genetic
algorithms. One recent example of using genetic algorithms in
biometric systems is in the area of face recognition [3]. The
approach presented in the paper I’m reviewing optimizes a
fingerprint system by the use of a genetic algorithm. Through
this the automatic optimization of the parameters of the system
is possible.
This paper is structured as follows: In the next section we
discuss the fundamentals of the fingerprint recognition
algorithm and genetic algorithm the authors used. Also the
design of the genetic optimizer for biometric systems is
presented in this section as well as an overview of the test
database, methodology, test results and a discussion of their
meaning. Section 3 gives an overview of our proposed
solution to the accuracy and error rate of fingerprint systems.
A summary of the paper and an outlook of future work are
given in section 4.
II.
FINGERPRINT AND GENETIC ALGORITHMS
A. BioGINA-Algorithm
There are two basic types of fingerprint matching
techniques: graph based and minutiae based. Graph based is
the matching of whirl structures and image correlation which is
commonly found in different biometric algorithms. Minutiae
based matching is used by the BioGINA algorithm. Its
matching algorithm uses a set of 15 different parameters to
define allowed deviations for minutiae locations and
orientations. During the matching process pairs of two
minutiae in the verification sample are compared against
minutiae pairs in the template. The first subset of the BioGINA
parameters defines the maximum allowed deviation percentage
for properties such as distance, ridge count between minutiae
and angle between minutiae orientations. Measured deviations
are then scaled into partial scoring values using a linear
equation whose coefficients are defined in the second
parameter subset. Other parameters then define minimum
parital score values for minutiae to be counted for the overall
score value as well as the maximum allowed deviations for
global orientations of the fingerprint images. The BioGINA
parameter set has been based on intuitive guesses only.
Evaluations of performance using these default settings have
shown that it has an error rate of 3.13% [4].
B. Genetic Algorithm
The mechanisms of natural evolution such as natural
selection, inheritance, mutation and recombination allow life
to adapt to its environmental conditions by gradual random
modifications of the genetic code. Similar strategies can be
used to adapt parameters of a dynamic system to certain
specified conditions. Genetic algorithms (GAs) are search
procedures that use the mechanics of natural selection and
natural genetics. The genetic algorithm, first developed by
John H. Holland [5] in the 1960's, allows computers to solve
difficult problems. It uses evolutionary techniques, based on
function optimization and artificial intelligence, to develop a
solution. The basic operation of a genetic algorithm is simple.
The algorithm is started with a set of solutions (represented by
chromosomes) called population. Solutions from one
population are taken and used to form a new population. This
is motivated by a hope, that the new population will be better
than the old one. Solutions which are selected to form new
solutions (offspring) are selected according to their fitness the more suitable they are the more chances they have to
reproduce [6].
The selection and reproduction cycle is repeated until a
user specified termination condition is reached, such as a
solution is found that satisfies the target criteria, an allocated
amount of time has been spent or the algorithm did not create
any new better solutions for a certain period of time.
C. Design of the genetic Optimizer
The BioGINA algorithm has to be extended by two
functions to control its parameter settings to allow for
optimization of the algorithm. To begin with, the genetic
algorithm has to know about the number and range of values
for all parameters. The second function changes the current
parameter set. By adding these two functions, the algorithm
can be implemented in an independent external application so
that it will be possible to exchange the biometric interface with
minimal effort and enable parameter optimizations on other
biometric systems and other modalities.
The basic
architecture of the genetic optimization system for general
biometric verification algorithms is shown in figure 2 [7].
Verification Competitions (FVC) from the years 2000, 2002,
and 2004 [8].
Since the training sets of the FVC databases turned out to
be too small to provide reliable results, the parameter set
trained on the BioGINA database was used for performance
evaluation on all databases. To compare the authentication
performance of the algorithm using the different parameters,
biometric error rates were used. The false non-match rate
(FNMR) specifies how often authentic persons are rejected by
the algorithm. How frequently non-authentic persons are
accepted by the algorithm is indicated by the false match rate
(FMR). The major point of interest in the error rate
characteristics is the equal error rate (EER). This is where
both error rates, FNMR and FMR, are equal.
Figure 2 - Architecture of the genetic optimizer for biometric systems
The optimization process starts with the creation of
an initial population with randomly selected parameter sets. In
order to achieve a wide exploration of the search space, about
10,000 individuals should be created. After the fitness values
for the initial population is determined, a smaller set of the
best solution candidates is selected to be the population for the
actual optimization iteration. The final optimization process is
done by a modified form of the steady state genetic algorithm
which creates exactly one new individual in each generation
using the following scheme:
1.
Fitness based selection of one or two parent
individuals
2.
Mutation or crossover of the selected parents.
3.
Calculate new fitness value
4.
Replace a low quality individual in the current
population
5.
Repeat steps 1 – 5 until termination condition is
reached
D. Experimental Setup
A new fingerprint database for the BioGINA algorithm was
created for the tests. The database contained 10 fingerprints
each of all 10 fingers of 12 test persons. For the tests, the
database was divided into two test sets, set A and set B,
containing 60 fingerprints each with 10 samples. Set B was
used as a basis for the calculation of an optimal parameter
vector using genetic algorithms. Set A was used for the
evaluation of the generated parameter set. In order to crossvalidate their initial results, the authors also tested the
optimized parameters with the data of the Fingerprint
E. Experimental Results
The second row in Table 2, titled standard, shows the
EERs of the training set B and the evaluation set A using
standard parameter set without genetic optimization on the
BioGINA database. The third row (optimized) contains the
EERs for set A and set B with optimization of the BioGINA
database. As the table shows, the EER based on the optimized
parameters is better than the EER without optimization. The
resulting improvement is about 25% for set A and 41% for set
B. Table 3 contains the EERs based on the databases of the
biometric FVC of the years 2000, 2002, and 2004 using both
standard parameters and genetically optimized parameters.
For each database an improvement could be achieved using
the genetic optimizer.
Table 2 – EERs using BioGINA database
Table 3 – EER using FVC databases of 2000, 2002, and 2004
III.
OUR PROPOSED SOLUTION
A fingerprint matching system based on a genetic
algorithm is indeed an improvement over the current estimate
based systems. The study has shown that improvements can
be achieved with an algorithm that has the ability to adapt to
deformations and inexact transformations between different
fingerprints. The study also showed that a genetic based
algorithm is capable of finding the correspondences between
minutiae without resorting to an exhaustive search, which
leads to a more “robust” (better performing) system. In my
research for this paper, I have come to believe that in actual
practice, a biometric system based solely on a single biometric
feature may not be able to meet the practical performance
requirements. It is our belief that by integrating two or more
biometric features, overall verification performance may be
improved.
For example, it is well known that fingerprint verification
tends to have a much larger false reject rate, but a very low
false accept rate. Face recognition, on the other hand is not
very reliable in establishing a person’s true identity, but is
very efficient in searching large databases to find the top
matches. By combining these two, we may be able to reduce
the false reject rate of fingerprints while maintain the low false
accept rate. We feel that more research should be done in
possibly integrating two or more biometric measures which we
feel would prove out our theory.
IV.
CONCLUSION
This paper presented an optimization strategy for biometric
parameters using genetic algorithms. The test showed that
improvement was reached for all test databases including the
new BioGINA database and past FVC databases. The authors
plan on carrying out similar tests on other biometric modalities,
handwriting and speech, in the near future. I agree with the
authors of this paper that using genetic algorithms do indeed
increase the instance of false positive and false negative
authentications in fingerprint systems. The experiments carried
out by the authors have shown that genetic optimization does
indeed cause an improvement in fingerprint recognition
systems over other methods, such as estimation. As more
research and work is done in the area of artificial intelligence, I
believe that genetic algorithms will play a large part in system
intelligence. Genetic algorithms allow a system to adapt to
changing environments and to provide a better computing
experience for both users and administrators of these systems.
ACKNOWLEDGMENT
The authors would like to take this opportunity to thank
Coronicca Oliver for her invaluable peer review and editorial
work on this article. Her insights were truly appreciated and
this paper would not have been possible without her assistance.
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