Reviewers:Pr<strong>of</strong>. Dr. Abdenour HADID, University <strong>of</strong> Oulu, FinlandPr<strong>of</strong>. Dr. Mark NIXON, University <strong>of</strong> Southampton, United KingdomExaminers:Pr<strong>of</strong>. Dr. Arun ROSS, West Virginia University, USAPr<strong>of</strong>. Dr. Bernadette DORIZZI, Telecom SudParis, FranceDr. Sami ROMDHANI, Morpho, France
2AbstractThis dissertation studies s<strong>of</strong>t biometrics traits, their applicability in different security and commercialscenarios, as well as related usability aspects. We place the emphasis on human facial s<strong>of</strong>tbiometric traits which constitute the set <strong>of</strong> physical, adhered or behavioral human characteristicsthat can partially differentiate, classify and identify humans. Such traits, which include characteristicslike age, gender, skin and eye color, the presence <strong>of</strong> glasses, moustache or beard, inheritseveral advantages such as ease <strong>of</strong> acquisition, as well as a natural compatibility with how humansperceive their surroundings. Specifically, s<strong>of</strong>t biometric traits are compatible with the humanprocess <strong>of</strong> classifying and recalling our environment, a process which involves constructions <strong>of</strong>hierarchical structures <strong>of</strong> different refined traits.This thesis explores these traits, and their application in s<strong>of</strong>t biometric systems (SBSs), andspecifically focuses on how such systems can achieve different goals including database searchpruning, human identification, human re–identification and, on a different note, prediction andquantification <strong>of</strong> facial aesthetics. Our motivation originates from the emerging importance <strong>of</strong>such applications in our evolving society, as well as from the practicality <strong>of</strong> such systems. SBSsgenerally benefit from the non-intrusive nature <strong>of</strong> acquiring s<strong>of</strong>t biometric traits, and enjoy computationalefficiency which in turn allows for fast, enrolment–free and pose–flexible biometricanalysis, even in the absence <strong>of</strong> consent and cooperation by the involved human subjects. Thesebenefits render s<strong>of</strong>t biometrics indispensable in applications that involve processing <strong>of</strong> real lifeimages and videos.In terms <strong>of</strong> security, we focus on three novel functionalities <strong>of</strong> SBSs: pruning the search inlarge human databases, human identification, and human re–identification.With respect to human identification we shed some light on the statistical properties <strong>of</strong> pertinentparameters related to SBSs, such as employed traits and trait–instances, total categories,size <strong>of</strong> authentication groups, spread <strong>of</strong> effective categories and correlation between traits. Furtherwe introduce and elaborate on the event <strong>of</strong> interference, i.e., the event where a subject picked foridentification is indistinguishable from another subject in the same authentication group.Focusing on search pruning, we study the use <strong>of</strong> s<strong>of</strong>t biometric traits in pre-filtering largehuman image databases, i.e., in pruning a search using s<strong>of</strong>t biometric traits. Motivated by practicalscenarios such as time–constrained human identification in biometric-based video surveillancesystems, we analyze the stochastic behavior <strong>of</strong> search pruning, over large and unstructured datasets which are furthermore random and varying, and where in addition, pruning itself is not fullyreliable but is instead prone to errors. In this stochastic setting we explore the natural trade<strong>of</strong>f thatappears between pruning gain and reliability, and proceed to first provide average–case analysis<strong>of</strong> the problem and then to study the atypical gain-reliability behavior, giving insight on how <strong>of</strong>tenpruning might fail to substantially reduce the search space. Moreover we consider actual s<strong>of</strong>tbiometric systems (nine <strong>of</strong> them) and the corresponding categorization algorithms, and provide anumber <strong>of</strong> experiments that reveal the behavior <strong>of</strong> such systems. Together, analysis and experimentalresults, <strong>of</strong>fer a way to quantify, differentiate and compare the presented SBSs and <strong>of</strong>ferinsights on design aspects for improvement <strong>of</strong> such systems.With respect to human re–identification we address the problem <strong>of</strong> pose variability in surveillancevideos. Despite recent advances, face-recognition algorithms are still challenged when appliedto the setting <strong>of</strong> video surveillance systems which inherently introduce variations in the pose<strong>of</strong> subjects. We seek to provide a recognition algorithm that is specifically suited to a frontal-tosidere-identification setting. Deviating from classical biometric approaches, the proposed methodconsiders color- and texture- based s<strong>of</strong>t biometric traits, specifically those taken from patches <strong>of</strong>
- 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 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 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 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 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
- 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.