Thesis - Instituto de TelecomunicaÃ§Ãµes

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viii2.3.2 Voice . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 202.3.3 Keystroke . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 212.3.4 Gait . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 242.3.5 Pointer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 252.3.6 Eye . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 272.3.7 Heart . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 282.3.8 Brain . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 292.3.9 Related Techniques . . . . . . . . . . . . . . . . . . . . . . . . . . . . 302.4 Multimodal Biometrics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 312.5 Continuous Biometrics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 322.6 Discussion and Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . 323 Information Sources 353.1 Related Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 363.1.1 Human-Computer Interaction Recording Systems . . . . . . . . . . . 363.1.2 Signal Databases . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 373.2 Human Computer Interaction . . . . . . . . . . . . . . . . . . . . . . . . . . 373.2.1 Web Interaction Display and Monitoring . . . . . . . . . . . . . . . . 383.3 Physiological Signals . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 423.3.1 Electrodermal Activity . . . . . . . . . . . . . . . . . . . . . . . . . . 423.3.2 Electrocardiography . . . . . . . . . . . . . . . . . . . . . . . . . . . 483.4 Materials and Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 513.4.1 Participants . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 513.4.2 Experimental Setup . . . . . . . . . . . . . . . . . . . . . . . . . . . 523.4.3 Synchronization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 543.4.4 Signals . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 543.4.5 Experts and Data Annotation . . . . . . . . . . . . . . . . . . . . . . 573.4.6 Final Check-up . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 583.4.7 Material (The Tests) . . . . . . . . . . . . . . . . . . . . . . . . . . 583.5 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 583.6 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 594 Signal Processing andFeature Extraction 614.1 Human-Computer Interaction . . . . . . . . . . . . . . . . . . . . . . . . . . 614.1.1 Signal Processing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62
ix4.1.2 Feature Extraction . . . . . . . . . . . . . . . . . . . . . . . . . . . . 674.2 Electrodermal Activity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 684.2.1 Modeling Difficulties . . . . . . . . . . . . . . . . . . . . . . . . . . . 694.2.2 Electrodermal Models . . . . . . . . . . . . . . . . . . . . . . . . . . 704.2.3 Proposed Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 734.3 Electrocardiogram . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 844.3.1 Signal Processing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 844.3.2 Feature Extraction . . . . . . . . . . . . . . . . . . . . . . . . . . . . 904.4 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 915 Feature Selection andClassification 935.0.1 Notation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 935.1 Statistical Modeling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 945.1.1 Parametric Modeling . . . . . . . . . . . . . . . . . . . . . . . . . . . 945.1.2 Nonparametric Modeling . . . . . . . . . . . . . . . . . . . . . . . . 955.2 User Tuned Feature Selection . . . . . . . . . . . . . . . . . . . . . . . . . . 975.2.1 Feature Selection Implementation . . . . . . . . . . . . . . . . . . . 985.2.2 Computational Improvements on Feature Selection . . . . . . . . . . 995.3 Sequential Classification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1015.3.1 Sequential Classifier . . . . . . . . . . . . . . . . . . . . . . . . . . . 1025.4 Uncertainty Based Classification and Classifier Fusion . . . . . . . . . . . . 1065.4.1 Uncertainty Modeling . . . . . . . . . . . . . . . . . . . . . . . . . . 1065.4.2 Uncertainty Based Reject Option Classifier . . . . . . . . . . . . . . 1085.4.3 Uncertainty Based Classification Fusion . . . . . . . . . . . . . . . . 1105.4.4 Related Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1115.5 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1126 Applications and Results 1156.1 Pointer Dynamics Biometrics . . . . . . . . . . . . . . . . . . . . . . . . . . 1156.1.1 Data Preparation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1156.1.2 Feature Selection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1166.1.3 Authentication Results . . . . . . . . . . . . . . . . . . . . . . . . . . 1166.1.4 On Guessing Entropy . . . . . . . . . . . . . . . . . . . . . . . . . . 1186.2 Sympathetic Dynamics Biometrics . . . . . . . . . . . . . . . . . . . . . . . 1216.2.1 Data Preparation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 121
Page 1: UNIVERSIDADE TÉCNICA DE LISBOAINST
Page 6 and 7: iior gave some support to the devel
Page 9: ResumoO comportamento humano tem si
Page 14 and 15: x6.2.2 Feature Analysis . . . . . .
Page 16 and 17: xii4.2 The x-y representation of th
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Page 22 and 23: xviiiFCT Fundação para a Ciência
Page 25 and 26: Chapter 1Introduction1.1 The Proble
Page 27 and 28: 1.1. THE PROBLEM 3Figure 1.2: ECG s
Page 29 and 30: 1.2. MOTIVATION 5authentication sit
Page 31 and 32: 1.3. CONTRIBUTIONS 7resource in the
Page 33 and 34: 1.4. THESIS STRUCTURE 9The present
Page 35 and 36: Chapter 2State of the ArtIn this ch
Page 37 and 38: 2.1. BIOMETRICS CONCEPTS 13to class
Page 39 and 40: 2.1. BIOMETRICS CONCEPTS 15by the s
Page 41 and 42: 2.2. BIOMETRICS TECHNOLOGIES 17Figu
Page 43 and 44: 2.3. BEHAVIORAL BIOMETRICS 19Septem
Page 45 and 46: 2.3. BEHAVIORAL BIOMETRICS 21and co
Page 47 and 48: 2.3. BEHAVIORAL BIOMETRICS 232. ove
Page 49 and 50: 2.3. BEHAVIORAL BIOMETRICS 25Figure
Page 51 and 52: 2.3. BEHAVIORAL BIOMETRICS 27situat
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Page 55 and 56: 2.4. MULTIMODAL BIOMETRICS 312.4 Mu
Page 57 and 58: 2.6. DISCUSSION AND CONCLUSION 33al
Page 59 and 60: Chapter 3Information Sources - HCI
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3.2. HUMAN COMPUTER INTERACTION 39I
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3.2. HUMAN COMPUTER INTERACTION 41F
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3.3. PHYSIOLOGICAL SIGNALS 43Histor
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3.3. PHYSIOLOGICAL SIGNALS 45Figure
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3.3. PHYSIOLOGICAL SIGNALS 47Term D
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3.3. PHYSIOLOGICAL SIGNALS 49Figure
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3.4. MATERIALS AND METHODS 51Figure
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3.4. MATERIALS AND METHODS 53had th
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3.6. CONCLUSION 59set of computatio
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Chapter 4Signal Processing andFeatu
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4.1. HUMAN-COMPUTER INTERACTION 63I
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4.1. HUMAN-COMPUTER INTERACTION 65T
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4.1. HUMAN-COMPUTER INTERACTION 674
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4.2. ELECTRODERMAL ACTIVITY 69To ex
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4.2. ELECTRODERMAL ACTIVITY 71(eq.
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4.2. ELECTRODERMAL ACTIVITY 73model
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4.2. ELECTRODERMAL ACTIVITY 75the p
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4.2. ELECTRODERMAL ACTIVITY 77t 3 =
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4.2. ELECTRODERMAL ACTIVITY 79The d
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4.2. ELECTRODERMAL ACTIVITY 81the e
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4.2. ELECTRODERMAL ACTIVITY 831.00.
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4.3. ELECTROCARDIOGRAM 8511.111.010
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4.3. ELECTROCARDIOGRAM 87to the car
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4.3. ELECTROCARDIOGRAM 89analyzing
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4.4. CONCLUSION 918. a T - The ampl
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Chapter 5Feature Selection andClass
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5.1. STATISTICAL MODELING 95Weibull
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5.2. USER TUNED FEATURE SELECTION 9
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5.2. USER TUNED FEATURE SELECTION 9
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5.3. SEQUENTIAL CLASSIFICATION 101p
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5.3. SEQUENTIAL CLASSIFICATION 103s
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5.3. SEQUENTIAL CLASSIFICATION 105n
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5.4. UNCERTAINTY BASED CLASSIFICATI
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5.5. CONCLUSIONS 113of behavioral d
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Chapter 6Applications and ResultsTh
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6.1. POINTER DYNAMICS BIOMETRICS 11
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6.1. POINTER DYNAMICS BIOMETRICS 11
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6.2. SYMPATHETIC DYNAMICS BIOMETRIC
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6.3. HEART DYNAMICS BIOMETRICS 1316
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6.3. HEART DYNAMICS BIOMETRICS 133F
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6.4. CONCLUSION 135time(s) EDA EER
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Chapter 7ConclusionsIn this chapter
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7.2. APPLICATION SCENARIOS 139adequ
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7.3. FUTURE WORK 141the user clicks
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Appendix AToolsThe work underlying
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A.2. SCIENTIFIC COMPUTATION 145side
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Appendix BTestsThe set of tests dev
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B.1. INTELLIGENCE TEST 149Figure B.
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B.2. MEMORY TEST 151# 2 3 3# 2 3 3#
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B.2. MEMORY TEST 153Level. In this
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B.3. ASSOCIATION TEST 155Guide. Ini
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B.5. CONCENTRATION TEST 157Descript
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B.5. CONCENTRATION TEST 159Test Gen
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B.6. TEST’S SEQUENCE 161Orderpage
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B.7. RELATED DOCUMENTS 163Figure B.
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Bibliography[1] A. Acharya, M. Cutt
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BIBLIOGRAPHY 171[18] B. Bimbaum. US
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BIBLIOGRAPHY 173[42] H. D. Critchle
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BIBLIOGRAPHY 175[63] R. Fernandez a
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BIBLIOGRAPHY 177[84] H. Gamboa, A.
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BIBLIOGRAPHY 179[102] S. Hocquet, J
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BIBLIOGRAPHY 181[124] R. M. Kaplan
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BIBLIOGRAPHY 183[145] C. L. Lim, E.
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BIBLIOGRAPHY 185[169] NBSP. Interna
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BIBLIOGRAPHY 187[191] T. Pixley. Do
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BIBLIOGRAPHY 189[214] T. Shen and W
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BIBLIOGRAPHY 191[236] M. Trauring.
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