Thesis - Instituto de TelecomunicaÃ§Ãµes

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64 CHAPTER 4. SIGNAL PROCESSING AND FEATURE EXTRACTION100x−axis (pixels)500−500 0.5 1 1.5 2 2.5 3 3.5100y−axis (pixels)500−500 0.5 1 1.5 2 2.5 3 3.5Time (s)Figure 4.1: Input signals generated by the mouse move events.1008060y−axis (pixels)40200−20−40−60 −40 −20 0 20 40 60 80 100x−axis (pixels)Figure 4.2: The x-y representation of the signals. Closed dots (•) represent the input sampledpoints generated by mouse move events. Open dots (◦) represent linearly (equidistant)interpolated points. The line represents the smoothed spline interpolation.
4.1. HUMAN-COMPUTER INTERACTION 65To unwrap the angle vector (θ) removing the discontinuities near π and −π, we used thefollowing equations:( )θ i = arctan ∗ ∆y1+∆x 1{ ( ) }∆θ i = min ∆ arctan ∗ ∆yi+2kπ∆x ii∑j=1∆θ j, (4.3)k ∈ Zwhere the function arctan ∗ is the four quadrant arc-tangent of ∆x and ∆y, with thedomain [−π, π].The curvature is <strong>de</strong>fined as follows:c = ∆θ∆s . (4.4)The curvature is inversely proportional to the radius of the intrinsic circumference thatfits the point where the curvature is being calculated. The rate of change in the curvatureis computed using the following equation:Figure 4.3, left column, presents an example of these vectors.c ′ = ∆c∆s . (4.5)Temporal Information In the temporal domain we <strong>de</strong>fined 9 vectors, calculated fromthe original acquired data points:• px — the vector with the input px i . . . px n values.• py — the vector with the input py i . . . py n values.• t — the input time vector t i . . . t n .• v x — horizontal velocity.• v y — vertical velocity.• v — tangential velocity.
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UNIVERSIDADE TÉCNICA DE LISBOAINST
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iior gave some support to the devel
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ResumoO comportamento humano tem si
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viii2.3.2 Voice . . . . . . . . . .
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x6.2.2 Feature Analysis . . . . . .
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xii4.2 The x-y representation of th
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xiv
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xvi
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xviiiFCT Fundação para a Ciência
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Chapter 1Introduction1.1 The Proble
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1.1. THE PROBLEM 3Figure 1.2: ECG s
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1.2. MOTIVATION 5authentication sit
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1.3. CONTRIBUTIONS 7resource in the
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1.4. THESIS STRUCTURE 9The present
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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
Page 53 and 54: 2.3. BEHAVIORAL BIOMETRICS 29of sig
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
Page 61 and 62: 3.2. HUMAN COMPUTER INTERACTION 37t
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Page 65 and 66: 3.2. HUMAN COMPUTER INTERACTION 41F
Page 67 and 68: 3.3. PHYSIOLOGICAL SIGNALS 43Histor
Page 69 and 70: 3.3. PHYSIOLOGICAL SIGNALS 45Figure
Page 71 and 72: 3.3. PHYSIOLOGICAL SIGNALS 47Term D
Page 73 and 74: 3.3. PHYSIOLOGICAL SIGNALS 49Figure
Page 75 and 76: 3.4. MATERIALS AND METHODS 51Figure
Page 77 and 78: 3.4. MATERIALS AND METHODS 53had th
Page 83 and 84: 3.6. CONCLUSION 59set of computatio
Page 85 and 86: Chapter 4Signal Processing andFeatu
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Page 93 and 94: 4.2. ELECTRODERMAL ACTIVITY 69To ex
Page 95 and 96: 4.2. ELECTRODERMAL ACTIVITY 71(eq.
Page 97 and 98: 4.2. ELECTRODERMAL ACTIVITY 73model
Page 99 and 100: 4.2. ELECTRODERMAL ACTIVITY 75the p
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Page 103 and 104: 4.2. ELECTRODERMAL ACTIVITY 79The d
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Page 107 and 108: 4.2. ELECTRODERMAL ACTIVITY 831.00.
Page 109 and 110: 4.3. ELECTROCARDIOGRAM 8511.111.010
Page 111 and 112: 4.3. ELECTROCARDIOGRAM 87to the car
Page 113 and 114: 4.3. ELECTROCARDIOGRAM 89analyzing
Page 115 and 116: 4.4. CONCLUSION 918. a T - The ampl
Page 117 and 118: Chapter 5Feature Selection andClass
Page 119 and 120: 5.1. STATISTICAL MODELING 95Weibull
Page 121 and 122: 5.2. USER TUNED FEATURE SELECTION 9
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Page 125 and 126: 5.3. SEQUENTIAL CLASSIFICATION 101p
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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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Thesis - Instituto de TelecomunicaÃ§Ãµes

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