Table Of ContentNew Directions
in Behavioral
Biometrics
New Directions
in Behavioral
Biometrics
Khalid Saeed
with Marcin Adamski, Tapalina Bhattasali,
Mohammad K. Nammous, Piotr Panasiuk,
Mariusz Rybnik, and Soharab H. Shaikh
CRC Press
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Library of Congress Cataloging‑in‑Publication Data
Names: Saeed, Khalid (Computer scientist), author.
Title: New directions in behavioral biometrics / Khalid Saeed with Marcin
Adamski, Tapalina Bhattasali, Mohammad K. Nammous, Piotr Panasiuk, Mariusz
Rybnik, and Soharab H. Shaikh.
Description: Boca Raton : CRC Press, Taylor & Francis Group, 2017. | Includes
bibliographical references and index.
Identifiers: LCCN 2016016651 | ISBN 9781498784627
Subjects: LCSH: Biometric identification. | Behaviorism (Psychology)
Classification: LCC TK7882.B56 S236 2017 | DDC 006.2/48--dc23
LC record available at https://lccn.loc.gov/2016016651
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To my assistants and coauthors who have always
proved smart, outstanding, and prominent.
Contents
Foreword xiii
PreFace xv
acknowledgments xix
chaPter 1 IntroductIon to BehavIoral BIometrIcs 1
1.1 Behaviometrics 2
1.1.1 How It Works 2
1.1.2 Major Benefits 4
1.2 What Is Special about Behavioral Biometrics Data
Acquisition? 5
1.3 Behavioral Biometrics Features 5
1.4 Classification of Behavioral Biometrics Traits 6
1.5 Properties of Few Behavioral Biometrics 9
1.5.1 Signature 9
1.5.1.1 Constraints of Signature Recognition 11
1.5.1.2 Merits of Signature Recognition 11
1.5.1.3 Demerits of Signature Recognition 11
1.5.1.4 Applications of Signature Recognition 11
1.5.2 Keystroke Dynamics 12
1.5.2.1 Merits of Keystroke Recognition 13
1.5.2.2 Demerits of Keystroke Recognition 13
1.5.2.3 Application of Keystroke Recognition 14
1.5.3 Gait 14
1.5.3.1 Merits of Gait Recognition 15
1.5.3.2 Demerits of Gait Recognition 15
1.5.3.3 Application of Gait Recognition 15
vii
viii Contents
1.5.4 Voice 16
1.5.4.1 Differences between Voice and
Speech Recognition 17
1.5.4.2 Merits of Voice Recognition 17
1.5.4.3 Demerits of Voice Recognition 17
1.5.4.4 Applications of Voice Recognition 18
1.6 Behavioral Biometrics Data Acquisition Device 18
1.7 Behavioral Biometrics Recognition Systems 21
1.7.1 Accomplishment of Behavioral Biometrics
Systems 21
1.7.2 Initial Processing and Analysis
of Biometric Traits 21
1.7.3 Framework 22
1.8 Generalized Algorithm 23
1.9 Performance Measurement 24
1.9.1 Benchmark Definition 25
1.9.2 Robustness Analysis 25
1.9.3 Discussion 26
1.10 Evaluation of Behavioral Biometric Systems 27
1.10.1 Discussion 28
1.11 Comparison and Analysis 29
1.12 Human Measurement and Evaluation on the Basis
of Behavioral Biometric Features 30
1.12.1 Verification and Identification 31
1.12.2 Error Sources in Behavioral Biometrics 31
1.13 Types of Basic Verification Errors and Their Rates 32
1.13.1 Error Graphical Representation 32
1.13.2 Further Study of Errors 34
1.14 Open Issues 35
1.14.1 Collection of Sensitive Information 35
1.14.2 Negative Reaction to Obtrusive Equipment 35
1.14.3 Consent and Secondary Use for Data
Collected with Unobtrusive Equipment 35
1.14.4 Sensitivity to Change of Application
Configuration 36
1.14.5 Spoofing Attacks 36
1.15 Future Trends 36
1.16 Application Area 37
1.17 Behavioral Biometrics Used in Real-Time Application 37
1.18 Conclusions 39
References 39
chaPter 2 sIgnature recognItIon 41
2.1 Brief History of Handwriting Analysis 41
2.2 Automated Systems for Signature Recognition 42
2.3 Offline and Online Signatures 44
2.4 Types of Forgeries 47
Contents ix
2.5 Databases for Signature System Evaluation 48
2.5.1 SVC2004 50
2.5.2 GPDS-960 51
2.5.3 MCYT-100 51
2.5.4 BIOMET 52
2.6 Commercial Software 52
2.6.1 SOFTPRO 53
2.6.2 ParaScript 54
2.6.3 SQN Banking Systems 54
2.7 A Review to Signature Recognizers 54
2.7.1 Data Acquisition 55
2.7.2 Preprocessing 58
2.7.3 Feature Extraction 65
2.7.3.1 Graphology Based 68
2.7.3.2 Shape Context Based 69
2.7.3.3 Contour Based 71
2.7.3.4 Projection Based 72
2.7.3.5 Curvature Based 72
2.7.3.6 Radon Transform Based 72
2.7.3.7 Hough Transform Based 73
2.7.3.8 Texture Based 73
2.7.3.9 Wavelet Transform Based 74
2.7.4 Classification 75
2.7.4.1 Template Matching 75
2.7.4.2 Statistical Classification 77
2.8 Assessment of Biometric Signature Systems 79
2.9 Example Studies on Signature Recognition 79
2.9.1 Online System 79
2.9.1.1 Results 81
2.9.1.2 Identification 82
2.9.1.3 Verification 82
2.9.1.4 Discussion 82
2.9.2 Offline System 82
2.9.2.1 Results 85
2.9.2.2 Identification 85
2.9.2.3 Verification 86
2.9.2.4 Discussion 87
References 87
chaPter 3 keystroke dynamIcs 93
3.1 History of Keystroke Dynamics 93
3.2 Keystroke Analysis 94
3.2.1 Data Acquisition 95
3.3 Variability of Users, User Behavior, and Hardware 100
3.4 Authentication and Identification 101
3.4.1 On Biometrics Context of Keystroke Dynamics 103
x Contents
3.5 Characteristics of Keystroke Dynamics 105
3.5.1 Universality 106
3.5.2 Uniqueness 106
3.5.3 Permanence 108
3.5.4 Collectability 108
3.5.5 Performance 108
3.5.6 Acceptability 108
3.5.7 Circumvention 109
3.5.8 Summary 109
3.6 Approaches to Keystroke Dynamics 110
3.6.1 Taxonomies of Approaches 110
3.6.2 Input Text Approach Taxonomy 111
3.6.3 Simple Typing Features 111
3.7 Advanced Approaches 113
3.8 Fixed Text for All Users 115
3.8.1 Dataset 115
3.8.2 Proposed Algorithm 116
3.9 Fixed Text for Each User (BioPassword/
AdmitOneSecurity) 120
3.9.1 Computer-Access Security Systems Using
Keystroke Dynamics 120
3.9.2 AdmitOneSecurity 120
3.10 Non-Fixed Text with Regard to Key 121
3.10.1 Proposed Algorithm 121
3.10.2 Experimental Results and Discussion 123
3.11 Non-Fixed Text with No Regard to Key 126
3.11.1 Dataset 126
3.11.2 Proposed Algorithm 127
3.12 Continuous Authentication 128
3.13 Perspectives 128
3.14 Modern Trends and Commercial Applications
for Keystroke Dynamics 129
3.14.1 Errors Made by Users and Their
Correction Methods 129
3.14.2 Pressure-Sensitive Keyboards 130
3.14.3 Mobile Phone Keyboards 130
3.14.4 ATM Hardware 131
3.14.5 Random Numbers Generation 131
3.14.6 Timing Attacks on Secure Communications 131
3.14.7 Examples of Commercial Applications 131
3.15 Legal Issues 132
3.16 Conclusions 133
References 134
chaPter 4 gaIt analysIs 139
4.1 Human Gait Recognition 140
4.2 Features of Gait Analysis 140
