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11the color FERET dataset [Fer11] with frontal gallery images for training, and side (profile) probeimages for testing. Towards achieving re-identification, the proposed algorithm first analyzes thecolor and furthermore texture of the three patches, see Figure 3. Then we study the intensity correlationsbetween patches. This analysis is then followed by the construction of a single, strongerclassifier that combines the above measures, to re-identify the person from his or her profile, seeFigure 4.Figure 3: Frontal / gallery and profile / probe image of a subject. Corresponding ROIs for hair,skin and clothes color.10.90.80.70.6Perr0.50.40.30.20.102 4 6 8 10 12 14 16 18 20Subjects NFigure 4: Overall-classifier obtained by boosting color, texture and intensity differences.Deviating from the above security related applications, we consider then an application closerto entertainment, and specifically consider the application of soft biometrics in analyzing andquantifying facial aesthetics.Soft biometrics for quantifying and predicting facial aestheticsWith millions of images appearing daily on Facebook, Picasa, Flickr, or on different socialand dating sites, photographs are often seen as the carrier of the first and deciding impression of a
12person. At the same time though, human perception of facial aesthetics in images is a priori highlysubjective.We related among others soft biometric traits with this subjective human perception. In theprovided study we quantify insight on how basic measures can be used to improve photographs forCVs or for different social and dating websites. This helps create an objective view on subjectiveefforts by experts / journalists when retouching images. We use the gained objective view toexamine facial aesthetics in terms of aging, facial surgery and a comparison of average femalesrelatively to selected females known for their beauty. Specifically we provide intuition on therole of features, image quality and facial features, see Figure 5, in human perception. We usethese accumulated conclusions to construct a basic linear model that predicts attractiveness infacial photographs using different facial traits as well as image properties. We then examineand validate the designed metric. We employ the developed metric to conduct experiments andanswer questions regarding the beauty index in three cases: for famous attractive females, foraging females and in case of facial surgery. Finally we proceed to simulate, based on both, thepresented metric, as well as state of the art algorithmic accuracies an automatic tool for beautyprediction.Figure 5: Example image of the web site HOTorNOT,MOS = 9.8. The white disks represent thestress points, the red cross the image center.Practical implementation of soft biometrics classification algorithmsTowards practical implementation of the related concepts and ideas we develop a tool (concatenationof classification algorithms) for classification of facial soft biometric traits, where wespecifically emphasize on the most obvious facial identifiers, primarily mentioned by humans,when portraying an unknown individual. The constructed tool is streamlined to achieve reliabilityof identification at reduced complexity, and hence focuses on simple yet robust soft-biometrictraits, including hair color, eye color and skin color, as well as the existence of beard, moustacheand glasses, see Table 2. We then specifically focus on extraction and categorization of eye color,and present an additional study where we illustrate the influence of surrounding factors like illumination,eye glasses and sensors on the appearance of eye color.We create based on those traits a bag of six facial soft biometrics, see Figure 6, for which
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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
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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
Page 107 and 108: 105Appendix AAppendix for Section 3
Page 109 and 110: 107- We are now left withN −F = 2
Page 111 and 112: 109Appendix BAppendix to Section 4B
Page 113 and 114: 111Blue Green Brown BlackBlue 0.75
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 160 and 161: 13Table 2: Table of Facial soft bio
Page 162 and 163: 15Chapter 1PublicationsThe featured
Page 164 and 165: 17Bibliography[ACPR10] D. Adjeroh,
Page 166 and 167: 19[ZESH04] R. Zewail, A. Elsafi, M.
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FACIAL SOFT BIOMETRICS - Library of Ph.D. Theses | EURASIP

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