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68 5. FRONTAL-TO-SIDE PERSON RE–IDENTIFICATIONhair, skin and clothes. Towards providing insight, the work presented different identification experimentsthat adhere to the frontal–to–side setting, as well as presented a preliminary analyticalstudy that seeks to impart intuition on the role of the above traits in improving algorithmic reliability.Our analysis described the overall error probability, both as a function of collisions andof erroneous categorizations for given sizes of authentication groups. In the presence of a moderatereliability of the patches-based method, the analysis suggests promising applications of thismethod in settings such as pruning of searches.After the analysis of the three security related applications of human identification, pruningthe search and human re–identification in the chapters 3, 4 and 5, in the following chapter deviatefrom security and introduce a commercial applications of female facial aesthetics. We note thatin the employment of a SBSs, the system remains the same, solely the last analytic step changeswhen moving from security to entertainment.
69Chapter 6Soft biometrics for quantifying andpredicting 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 aperson. At the same time though, human perception of facial aesthetics in images is a priori highlysubjective. The nature of this perception has long been explored separately from psychologicaland photographical points of view, respectively focusing on the properties of the subject and ofthe image. The photographical point of view, corresponding to photo-quality assessment andenhancement, has recently attracted further attention, partly due to the vast amount of digitalimages that are now available, as well as due to the ease with which digital image manipulationcan now be achieved.6.1 Related workThe present work draws from former work in three areas, namely classical facial aesthetics,photo–quality and aesthetics and image processing based face recognition.6.1.1 Facial aestheticsThere are substantial amounts of works, both from psychological and sociological points ofview, studying human perception of facial attractiveness and beauty. Such perception is highlysubjective and is influenced by sociological and cultural factors and furthermore by individualpreferences. Although appreciation of beauty is subjective or in other words "beauty is in the eyeof the beholder", there are some characteristics that scientists have identified to evoke superiorpleasure when looking at. One such characteristic generally associated to beauty and perfection isthe golden ratio ϕ ≈ 1.6180339887. When this divine proportion appears in both nature or art,they are perceived harmonic and aesthetic, see [Doc05]. An attractive human face contains ϕ inseveral proportions, e.g face height / width and face height / location of eyes, see Figure 6.1.A further main characteristic is symmetry, which was evolutionary beneficial in its direct analogyto health [McK06]. Another sign for health and fertility is averageness of facial characteristics,not to be confused with faces of average persons. In [LR90] the authors present a studyshowing that mathematically average faces are considered beautiful. This study though contradictswith other theorems stating that attractiveness implies distinctive facial features. In termsof such features in literature following specifications are associated with beauty: a narrow face,
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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
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
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: 6710.90.80.70.6Perr0.50.40.30.20.10
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 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,
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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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