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40 3. BAG OF FACIAL SOFT BIOMETRICS FOR HUMAN IDENTIFICATION– Quantification error: the discrete values of soft biometric traits are mapped onto a limitedamount of bins and cause such an error. A lower amount of bins corresponds to less misclassificationsat the cost though of decreasing distinctiveness of this trait, as elaboratedabove.– The nomenclature of traits varies and is ambiguous. For example a hair color that might bedenoted with "red" can be labeled by different subjects as a variety of synonyms: auburn,orange, copper, reddish; but also as a completely different trait e.g. brown. A related studyestablishing labels for soft biometric traits with the Mechanical Turk was recently conductedby the authors in [CO11].– Different people perceive different traits (e.g. colors) differently. Specifically if the witnessof a crime has a different color understanding than the SBS performing the search it canlead to an erroneous search. This aspect though can be minimized if the witness is asked topoint at reference colors than just human labeling.– The awareness of people can be bad or wrong in how they remember traits.– Often occurring mixed categories like red-brown for hair color can be challenging for all,human perception, the SBS - training and - classification step.To visualize just the quantification error introduced by a human understandable SBS we havethe following simulation. We display in Figure 3.8 on the one hand purely the collision probabilityof subjects with 8 quantification bins(=traits instances) of hair color (light blond, dark blond, red,brown, black, grey, white and bald). On the other hand we have the re–identification of non–quantified and discrete computer–to–computer search. It is of interest, that even in the presence ofan estimation error the over all error probability is decreased in the absence of quantification error.A full computer–to–computer is presented in Chapter 5 and specifically in Figure 5.4, where theperformance of a SBS employing AdaBoost [FHT98] boosted algorithms for hair, skin and clothscolor, their textures and patch histograms is illustrated. The system is used for frontal–to–sidere–identification.10.90.80.7P err0.60.50.40.3Error probability of non−quantification, estimation error prone re−identificationCollision probability of 8−bin−quantified re−identification0.22 4 6 8 10 12 14 16 18 20Subjects NFigure 3.8: Re–identification error for hair color in ann-sized authentication group.3.6 SummaryIn this chapter we explored the use of multi-trait SBSs for human identification, studying analyticallythe relationship between an authentication group v, its size n, the featured categories ρ,and the effective categories F . Then we proceeded to show that in the interference limited setting,
41for a given randomly chosen authentication group v, of a given size n, the reliability of identification(averaged over the subjects in v) is a function only of the number of non-empty categoriesF(v). Furthermore we provided statistical analysis of this reliability, over large populations. Thelatter part provided bounds that, in the interference limited setting suggest an exponential reductionin the probability of interference patterns, as a result of a linear increase in ρ. Finally wemade some observations regarding algorithmic estimation and gave an example of how to counteract,given known population statistics.Having analyzed in this chapter pertinent measures and dynamics in the process of humanidentification based on SBSs, we proceed in the next chapter 4 to study the process of employingSBSs for pruning a large database search. The goal will be then not to identify a subject, but ratherto pre-filter such a large database for a consecutive processing with a more reliable algorithm, e.g.classical face recognition.
Page 1: FACIAL SOFT BIOMETRICSMETHODS, APPL
Page 5: AcknowledgementsThis thesis would n
Page 8: 6hair, skin and clothes. The propos
Page 11 and 12: 97 Practical implementation of soft
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: 39Table 3.4: Example for a heuristi
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
Page 59 and 60: 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
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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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