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92 7. PRACTICAL IMPLEMENTATION OF SOFT BIOMETRICS CLASSIFICATION ALGORITHMS
93Chapter 8User acceptance study relating to softbiometricsThe pervasiveness of biometric systems, and the corresponding growth of the biometric market,see [usa11a], has successfully capitalized on the strength of biometric-based methods in accuratelyand effectively identifying individuals. As a result, modern state-of-the-art intrusion detectionand security systems include by default at least one biometric trait. It is the case thoughthat little emphasis has been given to better understanding user-acceptance and user-preferenceregarding such systems. Existing usability related works, such as in [CAJ03] and [LBCK03], focuson establishing functional issues in existing ATM machines, or on studying the influence ofuser interaction on the performance of fingerprint based systems (see [KED11]) and interfaces(see [RJMAS09]). Other interesting works (see [usa11b], [CG05], [CJMR09]), analyze possiblemethods that improve interface design. Our emphasis here is on providing insight on the attitudesand experiences of users towards novel and emerging biometric verification methods, andto explore whether such novel biometric technologies can be, in terms of user acceptance, validalternatives to existing prevalent PIN based systems. Our focus, in addition to considering thetraditional PIN based method, is to explore the usability aspects of systems based on classicalbiometrics such as fingerprint and face recognition, and to then proceed to study the usability ofsystems based on the emerging class of soft-biometric methods. Our evaluation is based on havingthe users rate and rank their experiences with different access methods.8.1 Usability of access control methodsA successful access control system must (see [BH90] and [Nie93]) incorporate common userunderstanding and knowledge in order to enable a natural and unconstrained interaction with theuser. Generally this interaction involves a number of different subtasks, which must be plannedand structured to allow for simplicity and comfort. The subsequent mapping of such tasks onto theinterface should be intuitive, obvious and user friendly. Furthermore such designs should impartfeedback in the form of descriptions or state indications (progress bar, message window). Finallythe selected design should be robust against human errors. The above considerations will be theguiding principles of our tests and of the corresponding usability study.
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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
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: 917.5 SummaryThis chapter presented
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
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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
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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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