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8 CONTENTS4.4 General setting . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 484.5 Statistical analysis using the method of types and information divergence . . . . 494.5.1 Typical behavior: average gain and goodput . . . . . . . . . . . . . . . . 534.6 Practical application of above analysis on proposed SBSs . . . . . . . . . . . . . 544.6.1 Simulations: the instantaneous pruning gain . . . . . . . . . . . . . . . . 544.7 Average case analysis of gain, reliability and computational-cost effects of pruning 564.7.1 Average pruning gain and related error . . . . . . . . . . . . . . . . . . . 564.7.2 Computational cost reduction . . . . . . . . . . . . . . . . . . . . . . . 584.8 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 605 Frontal-to-side person re–identification 615.1 Related work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 615.2 System model, simulations and analysis . . . . . . . . . . . . . . . . . . . . . . 625.2.1 Operational scenario . . . . . . . . . . . . . . . . . . . . . . . . . . . . 625.2.2 Factor 1. Number of categories that the system can identify . . . . . . . . 635.2.3 Factor 2. Category representation of the chosen set of subjects . . . . . . 635.2.4 Factor 3. Robustness of categories estimation . . . . . . . . . . . . . . . 645.2.4.1 Boosting of patch color . . . . . . . . . . . . . . . . . . . . . 655.2.4.2 Patch texture . . . . . . . . . . . . . . . . . . . . . . . . . . . 665.2.4.3 Patch histogram distance . . . . . . . . . . . . . . . . . . . . 665.2.5 Combined overall-classifier . . . . . . . . . . . . . . . . . . . . . . . . 675.3 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 676 Soft biometrics for quantifying and predicting facial aesthetics 696.1 Related work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 696.1.1 Facial aesthetics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 696.1.2 Beauty and image processing . . . . . . . . . . . . . . . . . . . . . . . . 706.1.3 Photo–quality and aesthetics . . . . . . . . . . . . . . . . . . . . . . . . 706.2 Contribution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 716.3 Study of aesthetics in facial photographs . . . . . . . . . . . . . . . . . . . . . . 716.3.1 Database . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 716.3.2 Photograph aesthetics . . . . . . . . . . . . . . . . . . . . . . . . . . . . 726.3.3 Facial characteristics . . . . . . . . . . . . . . . . . . . . . . . . . . . . 726.4 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 746.4.1 Effect of traits on theMOS rating . . . . . . . . . . . . . . . . . . . . . 746.4.2 Insight provided from empirical data . . . . . . . . . . . . . . . . . . . . 756.4.3 Interconnectedness of different traits . . . . . . . . . . . . . . . . . . . . 756.5 Model for facial aesthetics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 766.5.1 Validation of the obtained metric . . . . . . . . . . . . . . . . . . . . . . 766.6 Experiments with MOS ̂ . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 776.6.1 Metric verification on highly ranked females . . . . . . . . . . . . . . . 776.6.2 Dependence between beauty and age: FG–NET aging database . . . . . . 776.6.3 Facial surgery . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 786.7 Towards an automatic tool for female beauty prediction . . . . . . . . . . . . . . 796.8 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 80
97 Practical implementation of soft biometrics classification algorithms 817.1 Set of facial soft biometrics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 817.1.1 Classification algorithms . . . . . . . . . . . . . . . . . . . . . . . . . . 817.1.2 Experimental results . . . . . . . . . . . . . . . . . . . . . . . . . . . . 837.2 Eye color as a soft biometric trait . . . . . . . . . . . . . . . . . . . . . . . . . . 857.2.1 The human iris color . . . . . . . . . . . . . . . . . . . . . . . . . . . . 857.2.2 Related work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 857.3 Iris color classification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 857.3.1 Gaussian Mixture Models and color spaces . . . . . . . . . . . . . . . . 867.4 Influential factors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 877.4.1 Illumination variation . . . . . . . . . . . . . . . . . . . . . . . . . . . . 887.4.2 Glasses influence . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 897.4.3 Consideration of left and right eye color . . . . . . . . . . . . . . . . . . 897.4.4 Camera sensors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 907.5 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 918 User acceptance study relating to soft biometrics 938.1 Usability of access control methods . . . . . . . . . . . . . . . . . . . . . . . . 938.1.1 Testing Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . 948.1.2 Access methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 948.2 User study related results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 958.2.1 Ease of access, user friendliness, time to acquire and clarity . . . . . . . 958.2.2 Access of private computer vs. crowded environment access . . . . . . . 968.2.3 Time to Acquire, Ease of Use and Data Security . . . . . . . . . . . . . 978.2.4 Ranking of favorite access methods . . . . . . . . . . . . . . . . . . . . 978.3 Comparison of access control systems . . . . . . . . . . . . . . . . . . . . . . . 988.4 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 99Conclusion 99A Appendix for Section 3 105A.1 Proofs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 105B Appendix to Section 4 109B.1 Proofs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 109B.2 Confusion matrices and population characteristics for proposed systems . . . . . 110C Appendix for Section 6 113D Publications 115Bibliography 117
Page 1: FACIAL SOFT BIOMETRICSMETHODS, APPL
Page 5: AcknowledgementsThis thesis would n
Page 8: 6hair, skin and clothes. The propos
Page 13 and 14: 11Notations used in this workE : st
Page 15 and 16: 13Chapter 1IntroductionTraditional
Page 17 and 18: 15event of collision, which is of s
Page 19 and 20: 17ric. In Section 6.6 we employ the
Page 21 and 22: 19Chapter 2Soft biometrics: charact
Page 23 and 24: 21is the fusion of soft biometrics
Page 25 and 26: 23plied on low resolution grey scal
Page 27 and 28: 25Chapter 3Bag of facial soft biome
Page 29 and 30: 27In this setting we clearly assign
Page 31 and 32: 29Table 3.1: SBSs with symmetric tr
Page 33 and 34: 31corresponding to p(n,ρ). Towards
Page 35 and 36: the same category (all subjects in
Page 37 and 38: 3.5.2 Analysis of interference patt
Page 39 and 40: an SBS by increasing ρ, then what
Page 41 and 42: 39Table 3.4: Example for a heuristi
Page 43 and 44: 41for a given randomly chosen authe
Page 45 and 46: 43Chapter 4Search pruning in video
Page 47 and 48: 45Figure 4.1: System overview.SBS m
Page 49 and 50: 472.52rate of decay of P(τ)1.510.5
Page 51 and 52: 49to be the probability that the al
Page 53 and 54: 51The following lemma describes the
Page 55 and 56: 534.5.1 Typical behavior: average g
Page 57 and 58: 55n = 50 subjects, out of which we
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5710.950.9pruning Gain r(vt)0.850.8
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59for one person, for trait t, t =
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61Chapter 5Frontal-to-side person r
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63Figure 5.1: Frontal / gallery and
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6510.90.80.7Skin colorHair colorShi
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6710.90.80.70.6Perr0.50.40.30.20.10
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69Chapter 6Soft biometrics for quan
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71raphy considerations include [BSS
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73Figure 6.3: Example image of the
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75A direct way to find a relationsh
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77- Pearson’s correlation coeffic
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79shown to have a high impact on ou
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81Chapter 7Practical implementation
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834) Eye glasses detection: Towards
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857.2 Eye color as a soft biometric
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87Table 7.5: GMM eye color results
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89and office lights, daylight, flas
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917.5 SummaryThis chapter presented
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93Chapter 8User acceptance study re
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95Table 8.1: User experience on acc
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97scared of their PIN being spying.
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99Table 8.2: Comparison of existing
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101ConclusionsThis dissertation exp
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103Future WorkIt is becoming appare
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105Appendix AAppendix for Section 3
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107- We are now left withN −F = 2
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109Appendix BAppendix to Section 4B
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111Blue Green Brown BlackBlue 0.75
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113Appendix CAppendix for Section 6
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115Appendix DPublicationsThe featur
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117Bibliography[AAR04] S. Agarwal,
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119[FCB08] L. Franssen, J. E. Coppe
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121[Ley96] M. Leyton. The architect
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123[RN11] D. Reid and M. Nixon. Usi
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125[ZG09] X. Zhang and Y. Gao. Face
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2Rapporteurs:Prof. Dr. Abdenour HAD
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Biométrie faciale douce 2Les terme
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Biométrie faciale douce 4une perso
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Couleur depeauCouleur descheveuxCou
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Biométrie faciale douce 8Nous nous
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Biométrie faciale douce 103. Proba
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Biométrie faciale douce 12l’entr
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Biométrie faciale douce 14Figure 6
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Biométrie faciale douce 16pages 77
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Reviewers:Prof. Dr. Abdenour HADID,
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3hair, skin and clothes. The propos
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person in the red shirt”. Further
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7- Not requiring the individual’s
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9Probability of Collision10.90.80.7
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11the color FERET dataset [Fer11] w
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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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