Biometric system and data analysis: evaluation, data mining and optimization
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| Main Authors: | , |
|---|---|
| Format: | Book |
| Language: | English |
| Published: |
Berlin [u.a.]
Springer US
2009
|
| Online Access: | Inhaltsverzeichnis |
| Physical Description: | XX, 267 S. Ill., graph. Darst. |
| ISBN: | 9780387776255 0387776257 |
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MARC
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| 016 | 7 | |a 986860093 |2 DE-101 | |
| 020 | |a 9780387776255 |c Gb. : ca. EUR 81.59 (freier Pr.), ca. sfr 133.00 (freier Pr.) |9 978-0-387-77625-5 | ||
| 020 | |a 0387776257 |c Gb. : ca. EUR 81.59 (freier Pr.), ca. sfr 133.00 (freier Pr.) |9 0-387-77625-7 | ||
| 024 | 3 | |a 9780387776255 | |
| 028 | 5 | 2 | |a 12205608 |
| 035 | |a (OCoLC)635349265 | ||
| 035 | |a (DE-599)DNB986860093 | ||
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| 041 | 0 | |a eng | |
| 044 | |a gw |c XA-DE-BE | ||
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| 084 | |a ST 177 |0 (DE-625)143604: |2 rvk | ||
| 084 | |a 004 |2 sdnb | ||
| 100 | 1 | |a Dunstone, Ted |e Verfasser |4 aut | |
| 245 | 1 | 0 | |a Biometric system and data analysis |b evaluation, data mining and optimization |c Dunstone Ted ; Neil Yager |
| 246 | 1 | 3 | |a Biometric systems for data analysis |
| 246 | 1 | 3 | |a Biometric system for data analysis |
| 264 | 1 | |a Berlin [u.a.] |b Springer US |c 2009 | |
| 300 | |a XX, 267 S. |b Ill., graph. Darst. | ||
| 336 | |b txt |2 rdacontent | ||
| 337 | |b n |2 rdamedia | ||
| 338 | |b nc |2 rdacarrier | ||
| 700 | 1 | |a Yager, Neil |e Verfasser |4 aut | |
| 856 | 4 | 2 | |m Digitalisierung UB Passau |q application/pdf |u http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=017724529&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |3 Inhaltsverzeichnis |
| 999 | |a oai:aleph.bib-bvb.de:BVB01-017724529 | ||
Record in the Search Index
| _version_ | 1804139365478170624 |
|---|---|
| adam_text | Contents
Part I An Overview of Biometrics
1
An Introduction to
Biometrie Data
Analysis
....................... 3
1.1
Introduction
............................................... 3
1.2
A Brief History of Automated
Biometrie
Identification
........... 4
1.2.1
The Hands: Fingers, Palms and Hands
................... 5
1.2.2
The Head: Face, Voice and Eyes
........................ 7
1.2.3
Other Biometrics
.................................... 9
1.2.4
Post September
11, 2001.............................. 9
1.3
Identity and Risk Management
............................... 10
1.4
Desirable
Biometrie
Attributes
............................... 11
1.5 Biometrie Data............................................. 13
1.5.1
Raw Data
........................................... 14
1.5.2
Token Data
......................................... 14
1.5.3
Template Data
....................................... 14
1.5.4
Metadata
........................................... 16
1.6 Biometrie
System Overview
................................. 16
1.6.1
Negative Identification
............................... 18
1.6.2
Common
Biometrie
Processes
......................... 18
1.6.2.1 Biometrie
Specific Processes
.................. 18
1.6.2.2 Biometrie
Independent Processes
............... 19
1.7
System Performance Graphs
................................. 19
1.8
Privacy
................................................... 21
1.8.1
Privacy Challenges
................................... 22
1.8.2
Privacy Enhancing Techniques
......................... 22
1.8.3
Privacy Codes
...................................... 23
1.9
Conclusion
................................................ 24
References
..................................................... 25
Biometrie
Matching
Basics...................................... 27
2.1 Biometrie
Authentication: Example
1.......................... 27
2.1.1
Enrollment
.......................................... 28
2.1.2
The Correct User
.................................... 29
2.1.3
The Incorrect User
................................... 29
2.1.4
The Match Threshold
................................. 30
2.1.5
Matching Performance
................................ 31
2.1.6
Setting a Threshold
.................................. 33
2.1.6.1
The Equal Error Graph
....................... 33
2.1.6.2
The ROC Graph
............................. 34
2.2 Biometrie
Authentication: Example
2.......................... 35
2.2.1
Matching Data
...................................... 36
2.2.2
Ground Truth
....................................... 36
2.2.3
Calculating Error Rates and Graphs
..................... 37
2.3 Biometrie
Identification: Example
3........................... 38
2.3.1
Matching Data
...................................... 38
2.3.2
Candidate List
....................................... 40
2.3.3
Rank-based vs threshold-based candidate list membership
.. 41
2.4
Conclusion
................................................ 42
References
..................................................... 42
Biometrie Data ................................................ 45
3.1
Storage of
Biometrie Data................................... 45
3.1.1
Primary
Biometrie Data
Elements
...................... 46
3.1.2
Transactions
........................................ 47
3.1.3
Errors and Quality
................................... 47
3.1.4
Upgrades
........................................... 48
3.1.5
Data Security and Integrity
............................ 48
3.2
Standards
................................................. 50
3.2.1
Formats for Data Interchange
.......................... 50
3.2.2
General Standards
................................... 51
3.2.3
Applications Interoperability and Data Interchange
........ 52
3.2.4 Biometrie
Testing Standards
........................... 52
3.3 Biometrie Data
Examples
.................................... 53
3.3.1
Fingerprint
.......................................... 54
3.3.2
Facial Image
........................................ 56
3.3.3
Iris
................................................ 59
3.3.4
Speech
............................................. 61
3.3.5 3D
Facial geometry
.................................. 64
3.3.6
Vascular
............................................ 66
3.3.7
Keystroke
.......................................... 66
3.3.8
Signature
........................................... 67
3.3.9
Hand Geometry
..................................... 67
3.4
Conclusion
................................................ 67
References
..................................................... 68
Contents xv
4 Multimodal Systems............................................ 71
4.1
Advantages of
Multimodal................................... 71
4.1.1
Accuracy...........................................
72
4.1.2
Enrollment
.......................................... 72
4.1.3
Security
............................................ 73
4.2
Types of
Multimodal
Systems
................................ 73
4.2.1
Sources of Information
............................... 73
4.2.2
Modes of Operation
.................................. 74
4.3
Combination Techniques
.................................... 75
4.3.1
Feature Level Fusion
................................. 75
4.3.2
Score Level Fusion
................................... 76
4.3.2.1
Classification
............................... 76
4.3.2.2
Score Combination
........................... 77
4.3.3
Decision Level Fusion
................................ 78
4.4
Evaluation
................................................ 78
4.5
Conclusion
................................................ 79
References
..................................................... 79
5
Performance Testing and Reporting
.............................. 81
5.1
Introduction
............................................... 81
5.2
The Test Plan
.............................................. 83
5.2.1
Evaluation Types
.................................... 83
5.2.1.1
Technology Evaluation
....................... 84
5.2.1.2
Scenario Evaluation
.......................... 84
5.2.1.3
Operational Evaluation
....................... 85
5.2.2
Elements of an Evaluation
............................. 86
5.2.2.1
Primary Test Elements
........................ 86
5.2.2.2
Secondary Test Elements
...................... 87
5.2.3
Benchmarking Against Human Performance
............. 89
5.3
The Test Set
............................................... 89
5.3.1
Test Size and Results Confidence
....................... 89
5.3.2
Ground Truth
....................................... 90
5.3.3
Errors in the Test Set
................................. 91
5.3.3.1
Before Data Collection
....................... 92
5.3.3.2
After Data Collection
......................... 92
5.3.4
Data Collection
...................................... 92
5.3.5
Matching Issues
..................................... 93
5.3.5.1
Normalization
............................... 93
5.3.5.2
Adaptation
.................................. 94
5.4
Reporting
................................................. 94
5.5
Conclusion
................................................ 95
References
..................................................... 97
6
Definitions
.................................................... 99
6.1
General
................................................... 99
6.2
Biometrie Data
.............................................100
6.3
Biometrie
Systems
.........................................101
6.3.1
People
.............................................101
6.3.2
User Interaction with
a Biometrie
System
................102
6.3.3
System Types
.......................................102
6.3.4
Applications
........................................103
6.4
Biometrie
Evaluation
.......................................104
6.4.1
Evaluation Types
....................................104
6.4.2
Performance Measures
................................104
6.4.3
Graphs
.............................................105
6.4.4
The
Biometrie
Menagerie
.............................106
6.4.5
Vulnerability and Security Definitions
...................106
6.4.6
Statistical Terms
.....................................107
Part II The
Biometrie
Performance Hierarchy
7
System Evaluation: The Statistical Basis of
Biometrie
Systems
......
Ill
7.1
Verification
................................................112
7.1.1
Hypothesis Testing
*.................................112
7.1.1.1
Problem Formulation
.........................112
7.1.1.2
Decision Errors
..............................114
7.1.2
Performance Rates
...................................114
7.1.2.1
Representing Match Score Distributions
.........114
7.1.2.2
The False Match, False Non-Match and Equal
Error Rates
.................................116
7.1.2.3
Selecting an
EER
Point
* .....................117
7.1.3
Performance Graphs
..................................120
7.1.3.1
ROC Curves
................................120
7.1.3.2
DET
Curves
................................121
7.1.3.3
Interpreting ROC Curves
......................121
7.2
Identification
..............................................123
7.2.1
Identification vs Verification
...........................124
7.2.1.1
Dependence on Number of Enrollments
.........125
7.2.1.2
Score Normalization
.........................126
7.2.2
Identification Performance
............................127
7.2.2.1
Verification-Based Performance Metrics
.........128
7.2.2.2
Open-set vs Closed-set Identification
............129
7.2.2.3
The CMC Curve
.............................130
7.2.2.4
The Alarm Graph
............................132
7.2.3
Projecting Results to Large Database Sizes
*.............135
7.2.3.1
CMC Curves
................................136
7.2.3.2
Alarm Graphs
...............................138
7.3
Dealing with Uncertainty
....................................138
7.3.1
Systematic Errors
....................................139
7.3.2
Sampling Errors
.....................................139
7.3.3
Confidence Interval Interpretation
......................140
7.3.4
Computing Confidence Intervals
*......................141
7.3.4.1
Parametric Techniques
........................143
7.3.4.2
Non-parametric Bootstrapping
.................144
7.3.5
Boxplots
...........................................144
7.3.6
Calculating Sample Sizes
.............................145
7.4
Other Performance Measures
.................................147
7.4.1
Enrollment and Acquisition Errors
......................147
7.4.2
Template Pre-selection or Matching Subsets
..............149
7.5
Conclusion
................................................150
References
.....................................................151
Individual Evaluation: The
Biometrie
Menagerie
..................153
8.1
Individual Variation
.........................................154
8.1.1
Causes for Individual Variation
.........................155
8.1.1.1
Physiology
................................. 155
8.1.1.2
Behavior
................................... 156
8.1.1.3
Data Capture
................................ 157
8.1.2
Impact of Individual Variation
......................... 157
8.1.3
Quality Scores
....................................... 158
8.1.4
Individual Thresholds
................................ 160
8.2
The
Biometrie
Menagerie
.................................... 161
8.2.1
Notation
*..........................................164
8.2.2
User Performance Statistics
............................164
8.2.3
The Zoo Plot
........................................166
8.2.4
Goats, Lambs and Wolves
.............................167
8.2.4.1
Goats
......................................167
8.2.4.2
Lambs and Wolves
...........................168
8.2.4.3
Existence Test
*.............................169
8.2.5
Worms, Chameleons, Phantoms and Doves
...............170
8.2.5.1
Notation
*..................................170
8.2.5.2
Chameleons
.................................170
8.2.5.3
Phantoms
...................................170
8.2.5.4
Doves
......................................171
8.2.5.5
Worms
.....................................171
8.2.5.6
Existence test
*..............................171
8.3
Analysis Using the
Biometrie
Menagerie
.......................172
8.3.1
Goats, Lambs and Wolves
.............................172
8.3.2
Worms, Chameleons, Phantoms and Doves
...............173
8.3.2.1
Chameleons
.................................173
8.3.2.2
Phantoms
...................................174
8.3.2.3
Doves
......................................174
8.3.2.4
Worms
..................................... 174
8.4
Case Studies
...............................................174
8.4.1
Iris Recognition
.....................................175
8.4.2
Fingerprint Recognition
...............................177
8.4.3
Surveillance Systems
.................................177
8.5
Conclusion
................................................178
References
.....................................................179
9
Group Evaluation: Data Mining for Biometrics
....................181
9.1
Group Metadata
............................................183
9.1.1
User Level
..........................................183
9.1.2
Template Level
......................................184
9.1.2.1
User
.......................................184
9.1.2.2
Environment
................................185
9.1.2.3
System
.....................................185
9.1.3
Match Level
........................................185
9.1.4
Attribute Notation
...................................186
9.2
System Analysis Approach
..................................186
9.2.1
Splitting the Data
....................................187
9.2.2
Comparing Results
...................................188
9.3
Data Mining
...............................................188
9.3.1
Correlation Analysis
.................................189
9.3.2
Machine Learning
...................................190
9.3.3
Supervised Learning
.................................192
9.3.3.1
Decision Trees
..............................192
9.3.3.2
Problem Formulation
.........................194
9.3.4
Unsupervised Learning
...............................196
9.4
Dealing with Problem Groups
................................197
9.4.1
Physiological Problems
...............................198
9.4.2
Behavioral Problems
.................................198
9.4.3
Environmental and Equipment Problems
.................199
9.5
Limitations of Group-Level Analysis
..........................200
9.6
Conclusion
................................................201
References
.....................................................201
Part III Special Topics in
Biometrie Data
Analysis
10
Proof of Identity
...............................................205
10.1
Identity Document Systems
..................................205
10.1.1
Modes of Operation
..................................206
10.1.1.1
Front of House
..............................207
10.1.1.2
Back of House
..............................211
10.1.2
Estimating Levels of Fraud
............................212
10.1.2.1
Running the Experiment
*.....................213
10.1.2.2
Manual Investigation
.........................215
10.1.2.3
Estimating Fraud Levels
*.....................216
10.2
Using Biometrics as Legal Evidence
...........................217
10.2.1
Advantages of Biometrics
.............................218
10.2.2
Disadvantages of Biometrics
...........................219
10.2.3
Match Score Distributions
.............................219
10.3
Conclusion
................................................220
References
.....................................................221
Covert Surveillance Systems
.....................................223
11.1 Biometrie
Surveillance
......................................224
11.1.1
Surveillance Systems Overview
........................225
11.1.2
Surveillance Systems Challenges
.......................227
11.2
Site Design
................................................229
11.2.1
Site Selection
.......................................230
11.2.2
Camera Placement
...................................231
11.2.3
Covertness
..........................................232
11.3
Running Scenario and Operational Evaluations
..................233
11.3.1
Creating a Watchlist
..................................234
11.3.2
Recording Ground Truth
..............................236
11.4
Performance Evaluation
.....................................237
11.4.1
System Level Analysis
................................238
11.4.2
Resolving Ground Truth
..............................238
11.4.2.1
Image Labeling
..............................239
11.4.2.2
Post-Processing
.............................240
11.4.3
Performance Measures
................................241
11.4.3.1
Detection Rate Analysis
......................243
11.4.3.2
False Alarm Rate Analysis
....................244
11.4.3.3
Computing the Alarm Graph
...................244
11.5
Conclusion
................................................245
References
.....................................................246
Vulnerabilities
.................................................247
12.1
Introduction
...............................................248
12.2
History
...................................................249
12.2.1
Common Criteria
....................................249
12.2.2
Liveness Research
...................................250
12.3
Points of Attack
............................................250
12.4
Fraud
.....................................................254
12.4.1
Enrollment Fraud
....................................254
12.4.2
Covert Fraud
........................................254
12.4.3
Cooperative Fraud
...................................255
12.5
Assessing Vulnerabilities and Attack Methods
..................256
12.5.1
Attacker Strength
....................................257
12.5.2
The Test Object Approach
.............................258
12.6
Vulnerability Mitigations
....................................258
12.7
Conclusion
................................................260
xx Contents
References
.....................................................260
Index .............................................................263
|
| any_adam_object | 1 |
| author | Dunstone, Ted Yager, Neil |
| author_facet | Dunstone, Ted Yager, Neil |
| author_role | aut aut |
| author_sort | Dunstone, Ted |
| author_variant | t d td n y ny |
| building | Verbundindex |
| bvnumber | BV035670244 |
| classification_rvk | ST 177 |
| ctrlnum | (OCoLC)635349265 (DE-599)DNB986860093 |
| discipline | Informatik |
| format | Book |
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| id | DE-604.BV035670244 |
| illustrated | Illustrated |
| indexdate | 2024-07-09T21:42:56Z |
| institution | BVB |
| isbn | 9780387776255 0387776257 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-017724529 |
| oclc_num | 635349265 |
| open_access_boolean | |
| owner | DE-739 |
| owner_facet | DE-739 |
| physical | XX, 267 S. Ill., graph. Darst. |
| publishDate | 2009 |
| publishDateSearch | 2009 |
| publishDateSort | 2009 |
| publisher | Springer US |
| record_format | marc |
| spelling | Dunstone, Ted Verfasser aut Biometric system and data analysis evaluation, data mining and optimization Dunstone Ted ; Neil Yager Biometric systems for data analysis Biometric system for data analysis Berlin [u.a.] Springer US 2009 XX, 267 S. Ill., graph. Darst. txt rdacontent n rdamedia nc rdacarrier Yager, Neil Verfasser aut Digitalisierung UB Passau application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=017724529&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Dunstone, Ted Yager, Neil Biometric system and data analysis evaluation, data mining and optimization |
| title | Biometric system and data analysis evaluation, data mining and optimization |
| title_alt | Biometric systems for data analysis Biometric system for data analysis |
| title_auth | Biometric system and data analysis evaluation, data mining and optimization |
| title_exact_search | Biometric system and data analysis evaluation, data mining and optimization |
| title_full | Biometric system and data analysis evaluation, data mining and optimization Dunstone Ted ; Neil Yager |
| title_fullStr | Biometric system and data analysis evaluation, data mining and optimization Dunstone Ted ; Neil Yager |
| title_full_unstemmed | Biometric system and data analysis evaluation, data mining and optimization Dunstone Ted ; Neil Yager |
| title_short | Biometric system and data analysis |
| title_sort | biometric system and data analysis evaluation data mining and optimization |
| title_sub | evaluation, data mining and optimization |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=017724529&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
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