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110 B. APPENDIX TO SECTION 4we conclude thatlog− limN→∞ n/ρ logP(α 1|α 0 ) = ∑ fα 0,f I f ( α 1,fα 0,f).(B.7)Finally given that P(α,τ) = P(α 0 )P(α 1 |α 0 ), we conclude that −lim N→∞logn/ρ logP(α,τ) =D(α 0 ||ρp)+ ∑ f α 0,fI f ( α 1,fα 0,f).□Proof of Theorem 2: The proof is direct from the method of types (cf. [CT06]), which appliesafter noting that |V(τ)| ≤ n 2ρ ˙≤ e nδ ∀δ > 0, and that sup α∈V(τ) P(α) ≤ P(τ) ≤|V(τ)|sup α∈V(τ) P(α).□Proof of Theorem 3: The proof is direct by noting that for anyδ > 0, then for τ ≥ τ 0 we have− limN→∞logn/ρ P(|S| > (τ +δ)n ) >− limρ N→∞logn/ρ P(|S| > τn ρ ),(B.8)and similarly for τ < τ 0 we have− limN→∞logn/ρ P(|S| < (τ −δ)n ρ ) > − limN→∞logn/ρ P(|S| < τn ρ ).(B.9)□B.2 Confusion matrices and population characteristics for proposedsystemsLight eyesDark eyesLight eyes 0.9266 0.0734Dark eyes 0.0759 0.9238p C 0.3762 0.6238Table B.1: Confusion matrix related to ‘2e’: 2 eye color categoriesNo moustacheMoustacheNo moustache 0.8730 0.1270Moustache 0.2720 0.7280p C 0.7340 0.1623Table B.2: Confusion matrix related to ‘m’: moustache detectionNo glassesGlassesNo glasses 0.9283 0.0717Glasses 0.0566 0.9434p C 0.0849 0.0188Table B.3: Confusion matrix related to ‘g’: glasses detection
111Blue Green Brown BlackBlue 0.75 0.1875 0 0.0625Green 0.0714 0.7858 0.1428 0Brown 0.0526 0 0.8948 0.0526Black 0.0455 0.0455 0 0.909p C 0.3251 0.0511 0.2399 0.3839Table B.4: Confusion matrix and population statistics related to ‘4e’: 4 eye color categoriesNo moustache No moustache Moustache No moustacheNo glasses Glasses No glasses Glasses00 01 10 1100 0.8104 0.0626 0.1179 0.009101 0.0494 0.8236 0.0072 0.119810 0.2525 0.0195 0.6758 0.052211 0.0154 0.2566 0.0412 0.6868p C 0.7340 0.1623 0.0849 0.0188Table B.5: Confusion matrix and population statistics related to ‘mg’: moustache - glasses systemlight eyes light eyes dark eyes dark eyesno moustache moustache no moustache moustache00 01 10 1100 0.8089 0.1177 0.0641 0.009301 0.2520 0.6746 0.0200 0.053410 0.0663 0.0096 0.8065 0.117311 0.0207 0.0553 0.2513 0.6725p C 0.3371 0.0390 0.5591 0.0647Table B.6: Confusion matrix and population statistics related to ‘2em’: 2 eye color and moustacheclassificationlight eyes light eyes dark eyes dark eyesno glasses glasses no glasses glasses00 01 10 1100 0.8602 0.0664 0.0681 0.005301 0.0524 0.8741 0.0042 0.069310 0.0705 0.0054 0.8575 0.066211 0.0043 0.0716 0.0523 0.8715p C 0.3080 0.0681 0.5109 0.1130Table B.7: Confusion matrix and population statistics related to ‘2eg’: 2 eye color and glassesclassification Figure B.1: Category nomenclature for ‘2emg’
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FACIAL SOFT BIOMETRICSMETHODS, APPL
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AcknowledgementsThis thesis would n
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6hair, skin and clothes. The propos
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97 Practical implementation of soft
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11Notations used in this workE : st
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13Chapter 1IntroductionTraditional
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15event of collision, which is of s
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17ric. In Section 6.6 we employ the
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19Chapter 2Soft biometrics: charact
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21is the fusion of soft biometrics
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23plied on low resolution grey scal
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25Chapter 3Bag of facial soft biome
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27In this setting we clearly assign
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29Table 3.1: SBSs with symmetric tr
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31corresponding to p(n,ρ). Towards
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the same category (all subjects in
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3.5.2 Analysis of interference patt
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an SBS by increasing ρ, then what
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39Table 3.4: Example for a heuristi
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41for a given randomly chosen authe
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43Chapter 4Search pruning in video
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45Figure 4.1: System overview.SBS m
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472.52rate of decay of P(τ)1.510.5
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49to be the probability that the al
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51The following lemma describes the
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534.5.1 Typical behavior: average g
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55n = 50 subjects, out of which we
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5710.950.9pruning Gain r(vt)0.850.8
Page 61 and 62: 59for one person, for trait t, t =
Page 63 and 64: 61Chapter 5Frontal-to-side person r
Page 65 and 66: 63Figure 5.1: Frontal / gallery and
Page 67 and 68: 6510.90.80.7Skin colorHair colorShi
Page 69 and 70: 6710.90.80.70.6Perr0.50.40.30.20.10
Page 71 and 72: 69Chapter 6Soft biometrics for quan
Page 73 and 74: 71raphy considerations include [BSS
Page 75 and 76: 73Figure 6.3: Example image of the
Page 77 and 78: 75A direct way to find a relationsh
Page 79 and 80: 77- Pearson’s correlation coeffic
Page 81 and 82: 79shown to have a high impact on ou
Page 83 and 84: 81Chapter 7Practical implementation
Page 85 and 86: 834) Eye glasses detection: Towards
Page 87 and 88: 857.2 Eye color as a soft biometric
Page 89 and 90: 87Table 7.5: GMM eye color results
Page 91 and 92: 89and office lights, daylight, flas
Page 93 and 94: 917.5 SummaryThis chapter presented
Page 95 and 96: 93Chapter 8User acceptance study re
Page 97 and 98: 95Table 8.1: User experience on acc
Page 99 and 100: 97scared of their PIN being spying.
Page 101 and 102: 99Table 8.2: Comparison of existing
Page 103 and 104: 101ConclusionsThis dissertation exp
Page 105 and 106: 103Future WorkIt is becoming appare
Page 107 and 108: 105Appendix AAppendix for Section 3
Page 109 and 110: 107- We are now left withN −F = 2
Page 111: 109Appendix BAppendix to Section 4B
Page 115 and 116: 113Appendix CAppendix for Section 6
Page 117 and 118: 115Appendix DPublicationsThe featur
Page 119 and 120: 117Bibliography[AAR04] S. Agarwal,
Page 121 and 122: 119[FCB08] L. Franssen, J. E. Coppe
Page 123 and 124: 121[Ley96] M. Leyton. The architect
Page 125 and 126: 123[RN11] D. Reid and M. Nixon. Usi
Page 127 and 128: 125[ZG09] X. Zhang and Y. Gao. Face
Page 129: 2Rapporteurs:Prof. Dr. Abdenour HAD
Page 132 and 133: Biométrie faciale douce 2Les terme
Page 134 and 135: Biométrie faciale douce 4une perso
Page 136 and 137: Couleur depeauCouleur descheveuxCou
Page 138 and 139: Biométrie faciale douce 8Nous nous
Page 140 and 141: Biométrie faciale douce 103. Proba
Page 142 and 143: Biométrie faciale douce 12l’entr
Page 144 and 145: Biométrie faciale douce 14Figure 6
Page 146 and 147: Biométrie faciale douce 16pages 77
Page 148 and 149: Reviewers:Prof. Dr. Abdenour HADID,
Page 150 and 151: 3hair, skin and clothes. The propos
Page 152 and 153: person in the red shirt”. Further
Page 154 and 155: 7- Not requiring the individual’s
Page 156 and 157: 9Probability of Collision10.90.80.7
Page 158 and 159: 11the color FERET dataset [Fer11] w
Page 160 and 161: 13Table 2: Table of Facial soft bio
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15Chapter 1PublicationsThe featured
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17Bibliography[ACPR10] D. Adjeroh,
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19[ZESH04] R. Zewail, A. Elsafi, M.
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