62 5. FRONTAL-TO-SIDE PERSON RE–IDENTIFICATIONWe here take a rather different and more direct approach in the sense that the proposed methoddoes not require mapping or a-priori learning. In applying this approach, we provide a preliminarystudy on the role <strong>of</strong> a specific set <strong>of</strong> simple features in frontal-to-side face recognition. <strong>Theses</strong>elected features are based on s<strong>of</strong>t biometrics, as such features are highly applicable in videosurveillance scenarios. To clarify, we refer to re-identification as the correctly relating <strong>of</strong> data toan already explicitly identified subject (see [MSN03]). Our used traits <strong>of</strong> hair, skin and clothescolor and texture also belong in this category <strong>of</strong> s<strong>of</strong>t biometric traits, and are thus <strong>of</strong> special interestfor video surveillance applications.5.2 System model, simulations and analysisIn what follows we empirically study the error probability <strong>of</strong> a system for extraction andclassification <strong>of</strong> properties related to the above described color s<strong>of</strong>t biometric patches. We firstdefine the operational setting, and also clarify what we consider to be system reliability, addressingdifferent reliability related factors, whose impact we examine. The clarifying experiments are thenfollowed by a preliminary analytical exposition <strong>of</strong> the error probability <strong>of</strong> a combined classifierconsisting <strong>of</strong> the above described traits.5.2.1 Operational scenarioThe setting <strong>of</strong> interest corresponds to the re-identification <strong>of</strong> a randomly chosen subject (targetsubject) out <strong>of</strong> a random authentication group, where subjects in gallery and probe images haveapproximately 90 degrees pose difference.Patches retrieval: We retrieve automatically patches corresponding to the regions <strong>of</strong> hair, skinand clothes, see Figure 5.1, based on the coordinates <strong>of</strong> face and eyes. The size <strong>of</strong> each patch isdetermined empirically by one half and one third distance <strong>of</strong> center-to-center eye distance. Thefrontal placement <strong>of</strong> the patches is following, horizontally / vertically respectively:– hair patch: from nose towards left side / top <strong>of</strong> the head,– skin patch: centered around left eye / centered between mouth and eyes,– clothes patch: from left side <strong>of</strong> the head towards left / mouth-(mouth-top <strong>of</strong> head distance).The horizontal side face placements <strong>of</strong> the patches are: nose-(nose-chin distance), eye towardsleft, head length-chin. Those coordinates were provided along with the images from the FERETdatabase, but are also easily obtainable by state <strong>of</strong> the art face detectors, like the OpenCV implementation<strong>of</strong> the Viola and Jones algorithm [VJ01a].Following the extraction <strong>of</strong> the patches we retrieve a set <strong>of</strong> feature vectors including the colorand texture information. In this operational setting <strong>of</strong> interest, the reliability <strong>of</strong> our system capturesthe probability <strong>of</strong> false identification <strong>of</strong> a randomly chosen person out <strong>of</strong> a random set <strong>of</strong> nsubjects. This reliability relates to the following parameters:– Factor 1. The number <strong>of</strong> categories that the system can identify, as in the previous chapters3 and 4 we refer to asρ.– Factor 2. The degree with which these features / categories represent the chosen set <strong>of</strong>subjects over which identification will take place.– Factor 3. The robustness with which these categories can be detected.Finally reliability is related (empirically and analytically) to n, where a higher n corresponds toidentifying a person among an increasingly large set <strong>of</strong> possibly similar-looking people.We will proceed with the discussion and illustration <strong>of</strong> the above three named pertinent factors.
63Figure 5.1: Frontal / gallery and pr<strong>of</strong>ile / probe image <strong>of</strong> a subject. Corresponding ROIs for hair,skin and clothes color.5.2.2 Factor 1. Number <strong>of</strong> categories that the system can identifyOur system employs s<strong>of</strong>t biometric traits, where each trait is subdivided into trait-instances,that directly have an impact on the number <strong>of</strong> overall categories ρ. We here note that in the scientificwork [SGN08] the terminology trait and semantic term was used instead. For this specificscenario, our system is endowed with the traits hair, skin and shirt color as well as texture andintensity correlation. It is to be noted that, as already shown in the chapters 3 and 4 the morecategories a system can classify subjects into, the more reliable the system is in terms <strong>of</strong> distinctiveness.We proceed with the description <strong>of</strong> population <strong>of</strong> categories.5.2.3 Factor 2. Category representation <strong>of</strong> the chosen set <strong>of</strong> subjectsThe distribution <strong>of</strong> subjects over the set <strong>of</strong> ρ categories naturally has an impact on the specificimportance <strong>of</strong> the different traits and thus also affects the behavior <strong>of</strong> the system for a givenpopulation. Table 5.1 and Table 5.2 give example distributions, with respect to skin and haircolor traits, based on265 subjects from the color FERET database. For clarifying experiments weattributed this subset into three subcategories <strong>of</strong> skin color and eight subcategories <strong>of</strong> hair color.It is evident that the trait hair color has a higher distinctiveness and thus greater importance thanskin color, since it has more instances in which the subjects are subdivided into. This observationis illustrated in Figure 5.2 and we follow with the explanation.Categories 1 2 3Skin Color 62.64% 28.66% 8.7%Table 5.1: Distribution <strong>of</strong> FERET subjects in the three skin color categories.Categories 1 2 3 4 5 6 7 8Hair Color 4.2% 26.8% 46.4% 3% 3% 3.4% 1.5% 11.7%Table 5.2: Distribution <strong>of</strong> FERET subjects in eight hair color categories.In the case <strong>of</strong> (re-)identification the only way to unambiguously recognize a person is an
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6hair, skin and clothes. The propos
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97 Practical implementation of soft
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- Page 87 and 88: 857.2 Eye color as a soft biometric
- Page 89 and 90: 87Table 7.5: GMM eye color results
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- Page 95 and 96: 93Chapter 8User acceptance study re
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- 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
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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.