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124 BIBLIOGRAPHY[usa00] Interactive test methods for audiovisual communications, 2000. ITU-T RecommendationP.920.[usa11a] BCC research market forecasting, the global biometrics market, 2011.[usa11b] NIST usability and biometrics, 2011.[VFT + 09] D. Vaquero, R. Feris, D. Tran, L. Brown, A. Hampapur, and M. Turk. Attribute-basedpeople search in surveillance environments. In Proceedings of WACV, 2009.[vio][VJ01a] P. Viola and M. Jones. Robust real–time face detection. In Proceedings of IEEEICCV, 2001.[VJ01b] P. Viola and M. Jones. Robust real-time face detection. In Proceedings of IEEEICCV, 2001.[WAL04] B. Wu, H. Ai, and R. Liu. Glasses detection by boosting simple wavelet features. InProceedings of ICPR, 2004.[WAS + 05] F. Wallhoff, D. Arsic, B. Schuller, G. Rigoll, J. Stadermann, and A. Stoermer. Hybridprofile recognition on the mugshot database. 2005. Proceedings of IEEE EU-ROCON.[WBSS04] Z. Wang, A. C. Bovik, H. R. Sheih, and E. P. Simoncelli. Image qulaity assessment:From error visibility to structural similarity. IEEE Transactions on Image Processing,13(P4):600–612, 2004.[WM07] H. Weda and Barbieri M. Automatic children detection in digital images. In Proceedingsof ICME, 2007.[WMR01] F. Wallhof, S. Mueller, and G. Rigoll. Recognition of face profiles from the mugshotdatabase using a hybrid connectionist / hmm approach. In "Proceedings of IEEEICASSP", 2001.[WPS06] F. Wolf, T. Poggio, and P. Sinha. Bags of Words. Citeseer, 2006.[WSB02] Z. Wang, H. R. Sheikh, and A. C. Bovik. No-reference perceptual quality assessmentof jpeg compressed images. In Proceedings of IEEE ICIP, 2002.[WYS + 02] H. Wu, G. Yoshikawa, T. Shioyama, S. Lao, and M. Kawade. Glasses frame detectionwith 3D Hough transform. In Proceedings of ICPR, volume 16, pages 346–349,2002.[XY04] Y. Xiao and H. Yan. Extraction of glasses in human face images. pages 1–23.Springer, 2004.[YHP11] J. Ylioinas, A. Hadid, and M. Pietikäinen. Combining contrast and local binarypattrens for gender classification. In Proceedings of SCIA, 2011.[ZESH04] R. Zewail, A. Elsafi, M. Saeb, and N. Hamdy. Soft and hard biometrics fusion forimproved identity verification. In Proceedings of MWSCAS, volume 1, pages I –225–8, 2004.
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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
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41for a given randomly chosen authe
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43Chapter 4Search pruning in video
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45Figure 4.1: System overview.SBS m
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472.52rate of decay of P(τ)1.510.5
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49to be the probability that the al
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51The following lemma describes the
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534.5.1 Typical behavior: average g
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55n = 50 subjects, out of which we
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5710.950.9pruning Gain r(vt)0.850.8
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59for one person, for trait t, t =
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61Chapter 5Frontal-to-side person r
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63Figure 5.1: Frontal / gallery and
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6510.90.80.7Skin colorHair colorShi
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6710.90.80.70.6Perr0.50.40.30.20.10
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69Chapter 6Soft biometrics for quan
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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: 123[RN11] D. Reid and M. Nixon. Usi
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 148 and 149: Reviewers:Prof. Dr. Abdenour HADID,
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.
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