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Optimization of biometric Fingerprint Recognition parameters using Genetic Algorithms

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CPSC - 6126 Fall 2014 Term Paper By Krishna Sindhuri Nagavolu
13th December, 2014

Optimization of biometric fingerprint recognition parameters using Genetic Algorithms

Abstract
This research paper discusses about parameter optimization for biometric fingerprint recognition with the use of genetic algorithms. An accurate access control system is very important in domains like identification checks at airports, government organizations, FBI’s, scientific working groups like NASA, US defense department and driver’s license office. The main reason these organizations prefer Biometrics is because it measures physiological characteristics like fingerprints, iris patterns, facial recognition, retina recognition, ear recognition and DNA analysis. Based on the features of the biometric sensors used, the system can detect whether a person is an authorized user or not. Though there are many other methods for identification, biometric sensors are considered to be very reliable and accurate. The main objective of this paper is to build a fingerprint recognizer that is fully automatic and can minimize the errors caused while matching the fingerprints. Keywords: Biometrics, genetic algorithms, Parameter optimization, fingerprints

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Optimization of biometric fingerprint recognition parameters using Genetic Algorithms Table of Contents

Page Number

Abstract ……………………………………………………………………………..2 1. Introduction…………………………………………………………………………4

2. Genetic Algorithms……………………………………………………………..5 3. Design of Genetic Optimizer……………………………………………….7 4. Experimental Setup……………………………………………………………9 5. Proposed Solution……………………………………………………………11

6. Conclusion…………………………………………………………………………12

7. Acknowledgments and References……………………………………13

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Optimization of biometric fingerprint recognition parameters using Genetic Algorithms 1. Introduction

Traditional identification methods have several disadvantages like identity theft or hacking and biometric methods are considered to be one of the solutions to overcome these disadvantages. Biometrics is a science of establishing the identity of an individual based on his or her physical or behavioral patterns. The most common and well-known biometric identification system used is fingerprint recognition. Although there are many other biometric identification systems attracting the modern world, fingerprint recognition is considered as a standard model. They are widely used because of their individuality and immutability. The main disadvantages of biometrics are fraud rate, insult rate and equal error rate (EER). Fraud rate is the rate at which a biometric system mistakenly authenticates a person as a user and insult rate is the rate at which the system mistakenly does not authenticate the correct user… When the fraud rate and the error rate are same, then it is said to be equal error rate (EER). In other words EER can be determined as a point where false reject rates are equal to false accept rates. This article provides a method where the biometric system uses genetic algorithms for fingerprint recognition that has very low EER and is more robust. For more information on Biometrics refer [3]. Optimization is a process of making a system as effective as possible. Optimizations using “evolutionary approaches” are found in many technical systems today. The approach suggested by the author’s of the paper optimizes a fingerprint recognition system by using genetic algorithms. This paper focuses on the research done by Scheidat et al on “Parameter Optimization for biometric Fingerprint recognition using Genetic Algorithms”. The paper is organized as follows: In the second section an introduction to genetic algorithms is provided and how genetic algorithms can be used in biometrics is discussed later. The third section explains how to
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Optimization of biometric fingerprint recognition parameters using Genetic Algorithms design a genetic optimizer and the fourth section illustrates experimental setup and their results. The last section of the paper provides proposed solution, limitations of genetic algorithms and conclusion. 2. Genetic Algorithms John H. Holland first developed these in the 1960s in order to solve various problems related to computers. Genetic algorithms are considered large class of evolutionary algorithms that produce optimal solution using various techniques inspired by natural evolution such as mutation, crossover and inheritance. An example of genetic algorithms is where machines exhibit intelligence that mimics human solutions for a problem. The algorithms are started with a set of solutions, which are then inherited by new population. So it is assumed that the new population will be better and stronger than the old one and the solutions are selected according to their fitness (where fitness is the value of the objective function in the optimization problem being solved). Survival of the fittest comes into existence with these algorithms. Future generations are assumed to be more resistant than the older ones. For more information on genetic algorithms, biometrics and finger print recognition refer [3]. Genetic Algorithms in Biometrics - BioGINA ALGORITHM Fingerprint recognition and verification techniques are based on either minutiae features (a small, precise or trivial details of fingerprint) or graph based. Graph based is the matching of whirl structures and image correlation that is found in most of the biometric algorithms. Minutiae based are the matching of the location and direction of the ridge ending and bifurcations along a ridge path. For more detailed information on fingerprint recognition refer [3]. Minutiae matching are used by BioGINA algorithm. A fingerprint is defined by its uniqueness of the ridge characteristics and their relationships to
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Optimization of biometric fingerprint recognition parameters using Genetic Algorithms one another. Minutiae points are the local characteristics that occur either at the ridge ending or ridge bifurcation. Extracting these features from fingerprint images is curial step in fingerprint identification systems. Fingerprint identification systems initially detect the features and then match them with the existing template that contains the stored fingerprint images. When this algorithm was tested it showed significant improvement of 40% EER. For more information on fingerprints refer [7]. Different kinds of Minutiae features available for a fingerprint is given in the below figure:

The matching algorithm uses a set of 15 parameters to define allowed deviations for minutiae locations and orientations. In matching process, pairs of two minutiae in verification sample are compared against minutiae pairs that are available in the enrollment template. The first subset defines the percentage values for the maximum deviation allowed for relative properties of minutiae (like distance, ridge count). The measured deviations are then scaled into
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Optimization of biometric fingerprint recognition parameters using Genetic Algorithms partial scoring values using a linear equation, and the coefficients are then defined in second parameter subset. All the other parameters define minimum partial score values for minutiae to be counted for the overall score value as well as the maximum deviation allowed for the fingerprint images. All the parameters are correlated and it is hard to calculate the optimal values analytically. For best possible calibration extensive experiments would be required and hence the BioGINA parameter set has been based on intuitive estimation only. When the performance was measured using these default settings the EER was 3.13%. Further the systems performance can be improved by parameter tuning. 3. Design of Genetic Algorithm In order to optimize BioGINA algorithm, the biometric interface has to be extended by two functions to control parameter settings. Initially, the genetic algorithm has to be notified about the number and range of all the parameters, and then the second function is used to change the current parameter set. This will allow the genetic algorithm to be implemented in an external and independent application so that exchanging the biometric interface can be done with minimal effort, thereby enabling parameter optimizations on other biometric systems. The below figure shows the architecture of genetic optimizer that is used for biometric systems:

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Optimization of biometric fingerprint recognition parameters using Genetic Algorithms

To determine the fitness of a specific parameter set, biometric error rates can be transferred directly to fitness value. This represents an inverted quality measure, and the goal of the optimization process will be (in this case) minimizing the fitness value. A biometric database will be necessary to estimate error rates for fitness calculations. The actual optimization process starts with approximately creating 10,000 individuals with randomly selected parameter sets. The fitness values for this population is determined and a smaller set of best solution candidates are selected to be the population of the actual optimization iteration. The optimal population size depends on the speed of the matching algorithm and was set to 100 for BioGINA algorithm. A modified form of the steady state genetic algorithm that creates exactly one new individual in each generation in the following steps does the final optimization process:
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Optimization of biometric fingerprint recognition parameters using Genetic Algorithms • Selecting one or two parent individuals based on Fitness - Parent individuals are selected to generate one new individual for new generation, and the suitable candidates are determined by a fitness function. • The crossover or mutation of the selected parents - Reproduction rules are used for this mutation or crossover • Calculate the new fitness value of the child • Replace the low value individual in the current population to generate new generation. This step ensures that already found current best solutions are not lost accidentally until at least one better candidate is found. Repeat all the above steps until termination condition is reached. This termination process will be performed manually after at least 500 generations without measurable improvements. 4. Experimental Setup For testing purposes a new fingerprint database for BioGINA algorithm was created and in this database 10 fingerprints that were obtained from 10 fingers of the 12 test persons were taken. This data was collected using a capacitive Infineon FingerTIP sensor. The graphical data is of size 224 * 228 pixels and with a resolution of 513 dpi. The database was then subdivided into two test sets, Set A and Set B, which has 60 fingerprints each with 10 samples. Set A was used for the evaluation of the generated parameter set, and Set B was the basis for the calculation of an optimal parameter vector using genetic algorithms. By dividing into two different sets, training data and the evaluation data are strictly separated and over fitting is avoided. The initial results were cross-examined by testing the optimized parameters with the data of Fingerprint Verification Competitions (FVC) in 2000, 2002 and 2004. As the training sets of the FVC databases are too small to provide
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Optimization of biometric fingerprint recognition parameters using Genetic Algorithms the reliable results, a parameter set trained on the BioGINA database has been used for performance evaluation on all databases. The Fraud rate and insult rate are used to compare the performance of the algorithm. Results The results of the above experimental procedures are explained in the below tables. Parameter Set Standard Optimized Set B – training 0.0283 0.0167 Set A - evaluation 0.0313 0.0233

Table 1: EERs using BioGINA database The improvement is approximately 25% for Set A and 41% for Set B. Parameter Set Database 1 Database 2 Database 3 Database 4

FVC 2000 database Standard Optimized 0.062 0.0369 0.0281 0.0232 0.0754 0.0671 0.0793 0.0552

FVC 2002 database Standard Optimized
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0.0898 0.0759

0.0739 0.0704

0.1517 0.1165

0.0505 0.0379

Optimization of biometric fingerprint recognition parameters using Genetic Algorithms FVC 2004 database Standard Optimized 0.0783 0.0633 0.0763 0.0611 0.0495 0.0469 0.1074 0.082

Table 2: EER using FVC databases of 2000,2002 and 2004 Table 2 gives the EERs based on the databases of the Biometric Fingerprint Verification Competitions of 2000, 2002 and 2004 using standard and parameter optimized genetically. The best result is determined on the Database 1 of FVC 2000 that used parameter optimization genetically. The EER is 0.062 for standard parameters and 0.0369 for optimized parameter set. The improvement is approximately 40%. The lowest improvement is calculated in database 2 of FVC 2002 with 4.7%. For more detailed experimental setup refer [2]. 5. Proposed Solution In my opinion fingerprint recognition system based on genetic algorithm is definitely an improvement when compared to current estimate based systems. The studies have shown that improvements can be achieved with an algorithm that has the ability to adapt to deformations and inexact transformations between different fingerprints. The studies have also shown that a genetic algorithm is effective for finding the correspondences between minutiae, minimizing an in-depth search that leads to a better performing system. Limitations: Although, the solution mentioned in the paper by Scheidat et al had less EER compared to current estimate based systems, fingerprint algorithms such as minutiae extraction may require extensive computation and also considered impractical. In actual practice, a biometric system based on a
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Optimization of biometric fingerprint recognition parameters using Genetic Algorithms single biometric feature may not be able to meet the practical performance requirements, and hence by integrating two or more biometric features, the overall verification performance may be improved. For example, a fingerprint verification system tends to have large false reject rate and low false accept rate. However, face recognition might not be very reliable for identification but it is considered very efficient for searching large databases to find the matches (low false reject rate). By combining these two techniques a low false accept rate can be maintained as well as the false reject rate can be reduced. This approach can be explained in the paper presented in reference [11]. Researchers should concentrate more on developing a hybrid algorithm (a combination of two or more biometric measures) to build more effective and efficient biometric systems. The research done by Chia-Hung Lin et al explains how this can be more effective. A PSO-based classifier using fractal patterns and an adaptive PNN that are used to recognize fingerprints has proved to be very successful. In this approach the digital image processing is used to enhance the image, that is converted to binary image and it locates the reference point. The Katz’s algorithm is used to estimate the fractal dimensions from a two-dimensional image. For more information on this approach please refer [9]. This paper (research done by Scheidat et al] doesn’t explain how to deal with fake fingers and fake fingerprints. A research done by Heeseung Choi et al has explained how to deal with the fake fingerprints there by decreasing fraud rate. The paper describes several ways to detect fake fingerprints by using multiple static features. The representative features consist of power spectrum, a histogram, the directional contrast, the ridge thickness and other fingerprint ridge signals that can be obtained by fingerprint images. For more details please refer to [10]. 6. Conclusion

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Optimization of biometric fingerprint recognition parameters using Genetic Algorithms In this paper an optimizing strategy for biometric parameters using genetic algorithms is illustrated. As per the test databases using BioGINA database and the databases of past Fingerprint Verification Contests (FVC) show a significant improvement in performance. The genetic optimizer initially used a training set based on the BioGINA database to calculate the new parameter and the tests were performed for public FVC databases in years 2000, 2002 and 2004. For BioGINA evaluation an improvement of 25% is reached and an improvement of 40% has been achieved for database 1 of the FVC 2000. In future, the authors plan to test genetic algorithms in various biometric systems and modalities. Based on the test results I agree with the authors (Scheidat et al) of this paper that using genetic algorithms will improve the performance of fingerprint recognition systems by reducing the EERs. There are several other interesting experiments done by Saleh and Adhami about transforming fingerprints into a sequence of points in angle-curve space. Refer [8] below for more information on this. Genetic algorithms will definitely allow a system to act according to the changing environments. Although, there is a significant improvement in the result further research should be conducted on developing Hybrid algorithms that improves the accuracy of the biometric systems. To check with recent updates biometrics refer [4]. 7. Acknowledgments and References Acknowledgments I would like to thank the original authors Scheidat et al, Ting-Shou Yo, Heeseung Choi et al, Chia-Hung Lin et al, Saleh and Adhami for providing a broader view various fingerprint recognition techniques and their test results.
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Optimization of biometric fingerprint recognition parameters using Genetic Algorithms

References [1] Google for images [2] Tobias Scheidat, Andreas Engel and Claus Vielhauer, “Parameter optimization for fingerprint recognition using genetic algorithms” – paper published in ResearchGate. http://www.researchgate.net/publication/220942210_Parameter_optimization_f or_biometric_fingerprint_recognition_using_genetic_algorithms [3] Wikipedia for standard definitions – Biometrics, Fingerprint recognition and Genetic algorithms http://en.wikipedia.org/wiki/Biometrics http://en.wikipedia.org/wiki/Fingerprint_recognition http://en.wikipedia.org/wiki/Genetic_algorithm [4] International Biometric Group – http://findbiometrics.com/internationalbiometric-group-ibg-announces-november-13-webcast-and-release-of-thebiometric-market-and-industry-report-2009-2014/ [5] Information Security Principles and Practices by Mark Stamp (suggested textbook for our class) [6] Jain, A.K., Prabakar, S., Ross, A. Fingerprint Matching: Data Acquisition and Performance Evaluation. MSU Technical Report TR99-14, 1999. [7] Fingerprint Algorithms – 2004-2013 Jean-Francois Mainguet
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Optimization of biometric fingerprint recognition parameters using Genetic Algorithms http://fingerchip.pagesperso-orange.fr/index.htm [8] Triangulation and Genetic Algorithms for Fingerprint Matching by TingShou Yo on 01/15/2007 http://www.academia.edu/4597957/Triangulation_and_Genetic_Algorithms_fo r_Fingerprint_Matching [9] Combining Biometric Fractal Pattern and Practical Swarm OptimizationBased Classifier for Fingerprint Recognition by Chia-Hung Lin, Jian-Liung Chen, and Zwe-Lee Gaing http://www.hindawi.com/journals/mpe/2010/328676/ [10] Fake-fingerprint detection using multiple static features by Heeseung Choi, Raechoong Kang, Kyoungtaek Choi, Andrew Teoh Beng Jin and Jaihie Kim on April 2009 http://cherup.yonsei.ac.kr/files/Paper/Fake-fingerprint detection using multiple static features (2009).pdf [11] A Hybrid Fingerprint Multimatcher by S. Brahnam, C. Casanova, L. Nanni and A. Lumini http://www.academia.edu/2743046/A_Hybrid_Fingerprint_Multimatcher

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...Biometrics 125 years ago, winter 1879, Paris First agency police prefecture clerk Alphonse Bertillion was sitting in dark cold corner of huge file-cabinet room, where there were stored files on all Parisian criminals. A.Bertillion was entering the criminals’ description. He found out that there is no classification system, since: a) Criminals could easily change their names, and attempts to try different systems (by age, criminal method) end up in failure. Bertillion, son of Lui Bertillion – president of the Anthropological society, came up with an idea to utilize anthropological parameters (height, length and volume of the head, length of the hands, fingers, etc.) to identify personality. This was a break-through in criminalistics. With the development of electronics, Bertillioon’s theory was recalled and scientists realized that human beings have numerous unique characteristics beginning from the iris of the eye to the fingerprints and all these characteristics can be used in personality identification. With the rapid development of computer technologies, the concept of information security is becoming more and more important. Crime scene investigators have been using behavioral and physical characteristics for centuries in order to identify the criminals, nowadays; we have a great need for identification and authorization in information security. Information Security is being understood as a protection of the information and informational infrastructure...

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Biometrics

...Biometrics is a unique and effective security technology that authenticates a person’s identity by verifying personal characteristics. Biometric devices grant users access to programs, systems, or rooms by analyzing some biometric identifier, such as a fingerprint or eye pattern. Two commonly used types of biometric security devices are fingerprint readers and iris recognition systems. A fingerprint reader captures the curves and indentations of a fingerprint. Fingerprint readers can be set up to perform different functions for different fingers. For example, the index finger could start a program, and the ring finger could shut down the computer [ (Jenkins) ]. Fingerprint readers usually cost less than $100, making it easy for people to use them in personal and business environments. Newer computer models include internal fingerprint readers for added security. External fingerprint readers usually are connected to a USB port on the computer [ (Parlor) ]. A biometric security device used in high security areas is an iris recognition system. Iris recognition systems read patterns in the iris of the eye. These patterns are as unique as a fingerprint. Iris recognition systems are quite expensive and are used by government security organizations, the military, and financial institutions that deal with highly sensitive data [ (Doe) ]. Biometric technology is a very effective way to keep information secure. Biometric technology is used in several different ways. Two widely known...

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