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42 3. BAG OF FACIAL SOFT BIOMETRICS FOR HUMAN IDENTIFICATION
43Chapter 4Search pruning in video surveillancesystemsIn recent years we have experienced an increasing need to structure and organize an exponentiallyexpanding volume of data that may take the form of, among other things, images andvideos. Crucial to this effort is the often computationally expensive task of algorithmic searchfor specific elements placed at unknown locations inside large data sets. To limit computationalcost, pre-filtering such as pruning can be used, to quickly eliminate a portion of the initial data, anaction which is then followed by a more precise and complex search within the smaller subset ofthe remaining data. Such pruning methods can substantially speed up the search, at the risk thoughof missing the target, thus reducing the overall reliability. Common pre-filtering methods includevideo indexing and image classification with respect to color [SB91], patterns, objects [AAR04],or feature vectors [NJT06].4.1 Categorization-based pruningOur interest in analyzing this speed vs. reliability tradeoff, focuses on the realistic settingwhere the search is time-constrained and where, as we will see later on, the environment in whichthe search takes place is stochastic, dynamically changing, and can cause search errors. We notehere that there is a fundamental difference between search in unstructured versus structured data,where the latter can be handled with very efficient algorithms, such as the sphere decoding algorithm.One widely known practical scenario that adheres to the above stochastic setting, is thescenario of biometric-based video surveillance. In this setting a time constrained search seeksto identify a subject from within a large set of individuals that may consist of, for example, thepeople surrounding the subject in a specific instance at a specific location. In the language ofbiometrics we provide analysis on the general speed-reliability behavior in search pruning. In thisscenario, a set of subjects can be pruned by means of categorization that is based on differentcombinations of soft biometric traits such as facial color, shapes or measurements. The need forsuch biometrically-based search pruning often comes to the fore, such as in the case of the 2005London bombing and the 2011 London riots where a sizeable fraction of the police force workedfor days to screen a fraction of the available surveillance videos relating to the event.We stay focused on search pruning based on soft biometrics but remind the reader that thisanalysis can be generally applied to several domains of computer vision or other disciplines thatadhere to the setting of categorization-based pruning in time-constrained searches over error-pronestochastic environments. We clarify that we refer to pruning the search as the categorization and
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: 41for a given randomly chosen authe
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
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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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