102 8. USER ACCEPTANCE STUDY RELATING TO <strong>SOFT</strong> <strong>BIOMETRICS</strong>Applying s<strong>of</strong>t biometrics for person re-identification, with a focus on the frontal vs.side scenarioMotivated by realistic surveillance scenarios, the work addressed the problem <strong>of</strong> frontal-tosidefacial recognition, providing re–identification algorithms/classifiers that are specifically suitedfor this setting. Emphasis was placed on classifiers that belong in the class <strong>of</strong> s<strong>of</strong>t biometric traits,specifically color–, texture– and intensity– based traits taken from patches <strong>of</strong> hair, skin and clothes.Towards providing insight, the work presented different identification experiments that adhere tothe frontal–to–side setting, as well as presented a preliminary analytical study that seeks to impartintuition on the role <strong>of</strong> the above traits in improving algorithmic reliability. Our analysis describedthe overall error probability, both as a function <strong>of</strong> collisions and <strong>of</strong> erroneous categorizations forgiven sizes <strong>of</strong> authentication groups. In the presence <strong>of</strong> a moderate reliability <strong>of</strong> the patches-basedmethod, the analysis suggests promising applications <strong>of</strong> this method in settings such as pruning <strong>of</strong>searches.Applying s<strong>of</strong>t biometrics quantification and prediction <strong>of</strong> female facial aestheticsIn terms <strong>of</strong> female facial aesthetics, we presented a study on facial aesthetics in photographs,where we compared objective measures (namely photograph quality measures, facial beauty characteristicsand non permanent facial features), with human subjective perception. Our analysisrevealed a substantial correlation between different selected traits, and the corresponding MOSrelatedbeauty indices. Specifically we presented that non permanent features can influence highlythe MOS, and based on our analysis we conclude that facial aesthetics in images can indeedbe substantially modifiable. With other words parameters such as the presence <strong>of</strong> makeup andglasses, the image quality as well as different image post–processing methods can significantlyaffect the resulting MOS. Furthermore we constructed a linear MOS–based metric which wassuccessfully employed to quantify beauty-index variations due to aging and surgery. Our workapplies towards building a basis for designing new image-processing tools that further automateprediction <strong>of</strong> aesthetics in facial images. Towards this we provided a simulation <strong>of</strong> an automaticprediction tool based on state-<strong>of</strong>-art classification algorithms and the designed MOS–predictionmetric.The above approaches were accompanied by a more practically oriented part where we designedan automatic s<strong>of</strong>t biometrics classification tool. Specifically we focused on eye, skin andhair color, as well as on the presence <strong>of</strong> beard, moustache and glasses.In terms <strong>of</strong> usability analysis, we presented a user study investigating the preference <strong>of</strong> a set<strong>of</strong> test participants on access methods, namely s<strong>of</strong>t biometrics, face, PIN and fingerprint basedaccess methods. This preference was evaluated generally in terms <strong>of</strong> usability measures, suchas ease <strong>of</strong> use, intuitiveness and log-in-speed. Furthermore two scenarios were hereby assessed,specifically personal computer access and entrance <strong>of</strong> a security lab in a crowded environment.The surprising outcome is that although all users were strongly biased towards the PIN basedverification method, by daily use, the biometric based options were overall equally or even significantlybetter rated than the PIN based system. Users appreciated the comfort, easiness and speed<strong>of</strong> modern technology. Specifically they favored the s<strong>of</strong>t biometrics system, due to the providedprivacy preservation and ease <strong>of</strong> use.
103Future WorkIt is becoming apparent that surveillance will increasingly affect our quality <strong>of</strong> life and security.Research in this area has been embraced by both academia and industry. For this reason,security related biometric systems will become larger and more dynamic. We see the area <strong>of</strong> s<strong>of</strong>tbiometrics having from now on a solid position in such systems. Towards this we will need betterunderstanding <strong>of</strong> the component parts <strong>of</strong> such SBSs, and a corresponding better understanding <strong>of</strong>novel trait classification algorithms, as well as novel ways <strong>of</strong> combining and analyzing such algorithms.Our aim will be to allow for more efficient SBSs, but also develop a rigorous understanding<strong>of</strong> the capabilities and limits <strong>of</strong> such systems.Our aim in the future will also be, in addition to developing novel algorithms for SBSs, to alsoidentify and develop new commercial applications that can benefit by the power <strong>of</strong> s<strong>of</strong>t biometrics.
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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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- Page 63 and 64: 61Chapter 5Frontal-to-side person r
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- Page 67 and 68: 6510.90.80.7Skin colorHair colorShi
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- Page 71 and 72: 69Chapter 6Soft biometrics for quan
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- Page 75 and 76: 73Figure 6.3: Example image of the
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- 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
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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: 101ConclusionsThis dissertation exp
- 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
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- Page 144 and 145: Biométrie faciale douce 14Figure 6
- Page 146 and 147: Biométrie faciale douce 16pages 77
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- Page 150 and 151: 3hair, skin and clothes. The propos
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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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