Log In
Or create an account ->
Imperial Library
Home
About
News
Upload
Forum
Help
Login/SignUp
Index
Cover
Half Title
Title Page
Copyright Page
Dedication
Table of Contents
FOREWORD
PREFACE
ACKNOWLEDGMENTS
CHAPTER 1 INTRODUCTION TO BEHAVIORAL BIOMETRICS
1.1 Behaviometrics
1.1.1 How It Works
1.1.2 Major Benefits
1.2 What Is Special about Behavioral Biometrics Data Acquisition?
1.3 Behavioral Biometrics Features
1.4 Classification of Behavioral Biometrics Traits
1.5 Properties of Few Behavioral Biometrics
1.5.1 Signature
1.5.1.1 Constraints of Signature Recognition
1.5.1.2 Merits of Signature Recognition
1.5.1.3 Demerits of Signature Recognition
1.5.1.4 Applications of Signature Recognition
1.5.2 Keystroke Dynamics
1.5.2.1 Merits of Keystroke Recognition
1.5.2.2 Demerits of Keystroke Recognition
1.5.2.3 Application of Keystroke Recognition
1.5.3 Gait
1.5.3.1 Merits of Gait Recognition
1.5.3.2 Demerits of Gait Recognition
1.5.3.3 Application of Gait Recognition
1.5.4 Voice
1.5.4.1 Differences between Voice and Speech Recognition
1.5.4.2 Merits of Voice Recognition
1.5.4.3 Demerits of Voice Recognition
1.5.4.4 Applications of Voice Recognition
1.6 Behavioral Biometrics Data Acquisition Device
1.7 Behavioral Biometrics Recognition Systems
1.7.1 Accomplishment of Behavioral Biometrics Systems
1.7.2 Initial Processing and Analysis of Biometric Traits
1.7.3 Framework
1.8 Generalized Algorithm
1.9 Performance Measurement
1.9.1 Benchmark Definition
1.9.2 Robustness Analysis
1.9.3 Discussion
1.10 Evaluation of Behavioral Biometric Systems
1.10.1 Discussion
1.11 Comparison and Analysis
1.12 Human Measurement and Evaluation on the Basis of Behavioral Biometric Features
1.12.1 Verification and Identification
1.12.2 Error Sources in Behavioral Biometrics
1.13 Types of Basic Verification Errors and Their Rates
1.13.1 Error Graphical Representation
1.13.2 Further Study of Errors
1.14 Open Issues
1.14.1 Collection of Sensitive Information
1.14.2 Negative Reaction to Obtrusive Equipment
1.14.3 Consent and Secondary Use for Data Collected with Unobtrusive Equipment
1.14.4 Sensitivity to Change of Application Configuration
1.14.5 Spoofing Attacks
1.15 Future Trends
1.16 Application Area
1.17 Behavioral Biometrics Used in Real-Time Application
1.18 Conclusions
References
CHAPTER 2 SIGNATURE RECOGNITION
2.1 Brief History of Handwriting Analysis
2.2 Automated Systems for Signature Recognition
2.3 Offline and Online Signatures
2.4 Types of Forgeries
2.5 Databases for Signature System Evaluation
2.5.1 SVC2004
2.5.2 GPDS-960
2.5.3 MCYT-100
2.5.4 BIOMET
2.6 Commercial Software
2.6.1 SOFTPRO
2.6.2 ParaScript
2.6.3 SQN Banking Systems
2.7 A Review to Signature Recognizers
2.7.1 Data Acquisition
2.7.2 Preprocessing
2.7.3 Feature Extraction
2.7.3.1 Graphology Based
2.7.3.2 Shape Context Based
2.7.3.3 Contour Based
2.7.3.4 Projection Based
2.7.3.5 Curvature Based
2.7.3.6 Radon Transform Based
2.7.3.7 Hough Transform Based
2.7.3.8 Texture Based
2.7.3.9 Wavelet Transform Based
2.7.4 Classification
2.7.4.1 Template Matching
2.7.4.2 Statistical Classification
2.8 Assessment of Biometric Signature Systems
2.9 Example Studies on Signature Recognition
2.9.1 Online System
2.9.1.1 Results
2.9.1.2 Identification
2.9.1.3 Verification
2.9.1.4 Discussion
2.9.2 Offline System
2.9.2.1 Results
2.9.2.2 Identification
2.9.2.3 Verification
2.9.2.4 Discussion
References
CHAPTER 3 KEYSTROKE DYNAMICS
3.1 History of Keystroke Dynamics
3.2 Keystroke Analysis
3.2.1 Data Acquisition
3.3 Variability of Users, User Behavior, and Hardware
3.4 Authentication and Identification
3.4.1 On Biometrics Context of Keystroke Dynamics
3.5 Characteristics of Keystroke Dynamics
3.5.1 Universality
3.5.2 Uniqueness
3.5.3 Permanence
3.5.4 Collectability
3.5.5 Performance
3.5.6 Acceptability
3.5.7 Circumvention
3.5.8 Summary
3.6 Approaches to Keystroke Dynamics
3.6.1 Taxonomies of Approaches
3.6.2 Input Text Approach Taxonomy
3.6.3 Simple Typing Features
3.7 Advanced Approaches
3.8 Fixed Text for All Users
3.8.1 Dataset
3.8.2 Proposed Algorithm
3.9 Fixed Text for Each User (BioPassword/AdmitOneSecurity)
3.9.1 Computer-Access Security Systems Using Keystroke Dynamics
3.9.2 AdmitOneSecurity
3.10 Non-Fixed Text with Regard to Key
3.10.1 Proposed Algorithm
3.10.2 Experimental Results and Discussion
3.11 Non-Fixed Text with No Regard to Key
3.11.1 Dataset
3.11.2 Proposed Algorithm
3.12 Continuous Authentication
3.13 Perspectives
3.14 Modern Trends and Commercial Applications for Keystroke Dynamics
3.14.1 Errors Made by Users and Their Correction Methods
3.14.2 Pressure-Sensitive Keyboards
3.14.3 Mobile Phone Keyboards
3.14.4 ATM Hardware
3.14.5 Random Numbers Generation
3.14.6 Timing Attacks on Secure Communications
3.14.7 Examples of Commercial Applications
3.15 Legal Issues
3.16 Conclusions
References
CHAPTER 4 GAIT ANALYSIS
4.1 Human Gait Recognition
4.2 Features of Gait Analysis
4.3 Applications of Gait Analysis
4.4 Gait Cycle
4.5 Describing a Stance
4.6 Why Does Gait Change from Person to Person or from Time to Time?
4.7 A Brief Review of the Literature on Human Gait Recognition
4.8 Research Challenges
4.8.1 External Factors
4.8.2 Internal Factors
4.9 Gait Databases for Research
4.9.1 CASIA-A
4.9.2 CASIA-B
4.9.3 CMU MoBo
4.9.4 USF Dataset
4.9.5 Southampton Dataset
4.9.6 3D Dataset
4.9.7 UMD Dataset
4.9.8 TUM-IITKGP Dataset
4.9.9 OU-ISIR Database
4.10 Gait Recognition Using Partial Silhouette-Based Approach
4.10.1 Motivation of the Partial Silhouette-Based Approach
4.10.2 Dynamic Features of Gait—Why Partial Silhouette?
4.10.3 Partial Silhouette-Based Methodology
4.10.4 Preprocessing for Removing Noise
4.10.5 Gait Cycle Detection and Extraction of Landmark Frames
4.11 Extraction of Partial Silhouette
4.11.1 Bounding Box
4.11.2 Image Segmentation
4.11.3 Feature Extraction
4.11.4 Classification
4.11.5 Training
4.11.6 Testing
4.12 Experimental Verification
4.12.1 Results of Full versus Partial Silhouettes
4.13 Comparison with Other Methods
4.14 Effectiveness of Partial Silhouette Method in the Presence of Noise
4.15 Time Complexity of the Partial Silhouette-Based Method
4.16 Conclusions
References
CHAPTER 5 VOICE RECOGNITION
5.1 Voice Recognition
5.1.1 Advantages of Voice Recognition over Other Biometric Traits
5.1.2 Main Steps in Voice Recognition Systems
5.2 Signal Acquisition and Preprocessing
5.2.1 Biological Background
5.2.2 Preprocessing Stage
5.2.3 Feature Extraction
5.3 Toeplitz Matrix Minimal Eigenvalues Algorithm—A Survey
5.3.1 Linear Predictive Coding and Burg’s Model
5.3.2 Mel Frequency Cepstral Coefficients
5.4 Classification Using NNs
5.4.1 Probabilistic NNs
5.4.2 Radial Basis Function NNs
5.5 Achievements in Similar Works
5.6 Achievements in Voice Recognition
5.6.1 The Simplest Case, Uttered Words Recognition
5.6.1.1 Input Samples and Preprocessing Stage
5.6.1.2 Experiments and Result
5.6.2 Voiceprint and Security Systems
5.6.2.1 Performance of the Speaker Identification Security System
5.6.2.2 Multilevel Security for the Spoken Words and Speaker
5.6.3 Text-Independent Speaker Identification
5.6.3.1 Database and Preprocessing
5.6.3.2 First Attempt
5.6.3.3 Another Attempt
5.6.4 What about Speaker Verification?
5.6.4.1 Identification Treatment
5.6.4.2 Verify the Speaker—Claiming It Correctly
5.6.4.3 True Rejection and False Acceptance
5.6.4.4 Extra Testing Data for Verification
5.7 Conclusions
References
INDEX
← Prev
Back
Next →
← Prev
Back
Next →