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Fingerprint Recognition by Matching of Gabor Filter-Based Patterns

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Fingerprint Recognition by Matching of Gabor Filter-Based Patterns TECHNISCHE UNIVERSITÄT MÜNCHEN FAKULTÄT FÜR INFORMATIK Fingerprint Recognition by Matching of Gabor Filter-based Patterns Diplomarbeit in Informatik Markus Huppmann 2 e e e e e e e e FAKULTÄT FÜR INFORMATIK e e e e e DER TECHNISCHEN e e e e e e e UNIVERSITÄT MÜNCHEN Forschungs- und Lehreinheit IX Bildverstehen und wissensbasierte Systeme Fingerprint Recognition by Matching of Gabor Filter-based Patterns Diplomarbeit in Informatik Aufgabensteller : Prof. Bernd Radig Betreuer : Dipl. Inf. Matthias Wimmer Abgabedatum : 15. Januar 2007 4 Ich versichere, dass ich diese Diplomarbeit selbständig verfasst und nur die angegebe- nen Quellen und Hilfsmittel verwendet habe. München, den 15. Januar 2007 (Markus Huppmann) Abstract In the modern computerized world, it has become more and more important to authenticate people in a secure way. Modern applications like online bank- ing or online shopping use techniques that depend on personal identification numbers, keys, or passwords. Nevertheless, these technologies imply the risk of data being forgotten, lost, or even stolen. Therefore biometric authentication methods promise a unique way to be able to authenticate people. A secure and confidential biometric authentication method is the utilization of fingerprints. Usually a technique called minutiae matching is used to be able to handle au- tomatic fingerprint recognition with a computer system. This thesis proposes a different fingerprint recognition technique, which uses the matching of Gabor filter-based patterns. 6 Contents 1 Introduction 13 1.1 Importance of Authentication . 13 1.2 History of the Use of Fingerprints for Authentication . 13 1.3 Thesis Overview . 14 2 Technical Introduction 17 2.1 Biometric Systems . 17 2.1.1 Identification and Verification . 17 2.1.2 Biometric Authentication Techniques . 18 2.1.3 Introduction to Fingerprints . 19 2.1.4 Sensors . 20 2.1.5 Workflow of Biometric Systems . 21 2.2 Fingerprint Recognition Methods . 22 2.2.1 Minutiae Matching . 23 2.2.2 Pattern Matching . 25 2.2.3 Overview of Fingerprint Recognition Methods . 26 3 Pattern Matching Using Gabor Filters 29 3.1 Normalization . 30 3.2 Segmentation . 32 3.3 Reference Point Detection . 34 3.4 Gabor-Filter . 42 3.5 Creation of the Feature Map . 46 3.6 Matching . 49 4 Evaluation 53 4.1 Biometric Benchmarks . 53 4.2 Tests . 54 4.3 Conclusion of the Test Results . 58 5 Summary and Outlook 59 7 Contents 5.1 Summary . 59 5.2 Outlook . 59 8 List of Figures 2.1 The different fingerprint classes and their frequency of occurrence . 19 2.2 Two sensors from UPEK . 20 2.3 Recording of a fingerprint . 21 2.4 Low quality fingerprints . 22 2.5 Workflows: Identification, verification . 23 2.6 Minutiae . 23 2.7 Fingerprint with marked minutiae . 24 2.8 Ridge matching . 25 2.9 Filterbank-based matching . 26 3.1 Fingerprint before normalization . 30 3.2 Fingerprint after normalization . 31 3.3 Fingerprint segmentation . 32 3.4 Orientation angle θ of pixel (i,j) . 34 3.5 Local ridge orientation of a fingerprint . 36 3.6 Restricted region of interest . 37 3.7 Smoothed orientation map . 39 3.8 Results of the reference point detection . 40 3.9 Failures of the reference point detection . 41 3.10 Inter-ridge distance . 43 3.11 Filter directions of a Gabor filter . 44 3.12 Real part of the impulse response of a Gabor filter. 44 3.13 Imaginary part of the impulse response of a Gabor filter. 44 3.14 Gabor filter output . 45 3.15 Rectangular and circular tessellation . 46 3.16 Rectangular and circular tessellation superimposing a fingerprint . 47 3.17 Obtained feature maps . 48 3.18 Matching of two fingerprints of the same class . 51 3.19 Matching of two fingerprints of different classes . 52 3.20 Rotation during matching . 52 9 List of Figures 4.1 Result of test 1 . 56 4.2 Result of test 2 . 56 4.3 Result of the test with an alternative matching equation . 58 10 List of Tables 2.1 Overview of different biometric authentication techniques . 18 2.2 Overview of various matching algorithms . 27 4.1 Result of test 1 . 55 4.2 Result of test 2 . 55 4.3 Overview of all test results . 57 4.4 Result of the test with an alternative matching equation . 57 11 List of Tables 12 1 Introduction This chapter introduces into the basic concepts of authentication. After that, it reflects the history of the use of fingerprints for authentication. At last, an overview of this thesis is given. 1.1 Importance of Authentication Since the opening of the internet to commercial use at the beginning of the 1990s, handling financial transactions via online banking over the internet instead of personally going to the bank has become more and more common to modern people. Online shopping sales have also increased rapidly over the last few years. At the same time, it has remained difficult for the providers of these applications to assure that the users who carry out transactions or place orders in online shops are who they claim to be. Vice versa, it is more than unpleasant for the users if someone else orders on their behalf as fraud cases in a famous online auction house have shown in the past [1]. Therefore, several techniques have been developed in recent years to allow precise authentication of people. These techniques include the use of personal identification numbers (PIN), keys, or passwords. Unfortunately, it happens frequently that, for example, not only the owner of a credit card who is allowed to withdraw money knows the corresponding PIN. Nowadays, people have to memorize many numbers, pins, and passwords. Forgetful people are often not able to keep their specific code in mind. Furthermore, many people write down their PIN and store it along with their credit card in their wallet or keep their computer password written under their keyboard. In such cases, every technique that uses numbers, keys, or passwords becomes useless. Thus, it is evident that an assured and secure method for authentication has to be provided, which cannot be forgotten, lost, or even stolen. 1.2 History of the Use of Fingerprints for Authentication For this reason, using the biometrical attributes of a human being seems to be the per- fect way for unique authentication. At the end of the 19th century, fingerprints were used for the first time to identify people. In India, Sir William J. Herschel used fingerprints 13 1 Introduction to avoid double retirement payments to Indian workers and also to verify the identity of prisoners. In the process he collected thousands of fingerprints and after many years he was able to prove that fingerprints do not alter during lifetime. This consistency is an important reason for the use of fingerprints in addition to their uniqueness. Sir Fran- cis Galton developed a fingerprint classification and published his work in 1892 [2]. His book became the standard reference for many years. At the same time another system of authentication was developed based on the anatomi- cal attributes of people. This method is called anthropometry and chooses attributes such as the geometry of hand or measure of head to identify people. Those anatomic attributes, however, change during lifetime and are difficult to acquire. Therefore, the use of finger- prints became the leading way of identifying people. At the beginning of the 20th century, many criminal investigation departments all over the world started to use fingerprints to identify delinquents. After years of comparing fingerprints by hand, the FBI defined in 1969 the goal of the automation of fingerprint recognition, which led to the development of the "Integrated Automatic Fingerprint Iden- tification System (IAFIS)" [3]. This system became fully operational in 1998. But even to- day the FBI does not fully rely on this system and special trained agents verify the results by hand. Therefore, the recognition of fingerprints by automated computer systems has to be further developed. 1.3 Thesis Overview Almost every automated fingerprint authentication system today relies on the recogni- tion of singularities of a fingerprint called minutiae. The practical part of this thesis was the implementation of a new fingerprint recognition technique, which uses a Gabor filter- based pattern matching method instead of minutiae matching. Thus, the thesis is struc- tured as follows: Chapter 2 is an introduction to biometric systems, fingerprint recognition, and different techniques of fingerprint matching. Chapter 3 provides information on the different processing steps of the implemented Ga- bor filter-based pattern matching method. Chapter 4 shows the different matching results using this method on a fingerprint database. Chapter 5 outlines the results and offers an outlook on future developments. 14 1.3 Thesis Overview 15 1 Introduction 16 2 Technical Introduction As mentioned in Chapter 1, using fingerprints has become the generally accepted way to authenticate people and moreover these methods are cheap and fast. To this day, no human being has been found with the same fingerprint as another one. As technology becomes more and more advanced, specialized fingerprint sensors have become more af- fordable and provide better records of fingerprints. By contrast, authentication by means of DNA or the human iris takes a long time and is a very complex and expensive mat- ter. In the past, it was a laborious business to search for a fingerprint in a database by looking for similarities. Nowadays, computer technology provides us with the power to do that much faster and more reliably than before. Different ideas have been devel- oped performing fingerprint recognition automatically using computer systems. In this chapter different biometric systems will be presented and rated according to their advan- tages and disadvantages. Then, different methods of automatic fingerprint recognition are introduced.
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