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74 6. SOFT BIOMETRICS FOR QUANTIFYING AND PREDICTING FACIAL AESTHETICSaesthetics respectively.Table 6.1: Characteristics listed in decreasing order with respect to theabsolute Pearson’s correlation coefficient, related Pearson’s correlationcoefficients and related -model weights, see Figure 6.3 and Figure 6.4for notations of facial measuresTrait xiPearson’scorrelationcoefficient -Modelweightri,MOSx1. Ratio (eye height / head length) 0.5111f/a18.3506x2. Ratio (head width / head length) 0.4487b/a4.5780x3. Eye make up 0.3788 0.3055x4. Face shape 0.3521 0.1606x5. Eye Brow shape 0.2523 0.3337x6. Fullness of Lips 0.2242 0.2019x7. Ratio (from top of head to nose / 0.2198head length) (d+c)/a-17.8277x8. Glasses -0.2095 -0.6707x9. Lipstick 0.1997 0.0502x10. Skin goodness -0.1856 -0.3930x11. Hair Length / Style -0.1851 -0.0657x12. Ratio (from top of head to mouth 0.1818/ head length) (d+c+e)/a-4.1919x13. Ratio (from top of head to eye / 0.1774head length) d/a49.3939x14. Image format 0.1682 0.1695x15. Ratio (eye width / distance 0.1336 0.8982between eyes) (h-i)/(2.i)x16. Ratio (from nose to chin / eye to -0.1204 0.0970nose) (a-d-c)/cx17. Left eye distance to middle of 0.1183image or to mass point0.4197x18. Right eye distance to middle of 0.1155 0.2042image or to mass pointx19. Ratio (from top of head eye / eye -0.1012 -1.0091to nose) d/cx20. Image Resolution 0.1012 -0.3493x21. Expression -0.0913 -0.3176x22. Ratio (outside distance between -0.0833 -1.7261eyes / top of the head to eye) h/dx23. JPEG quality measure 0.0802 0.9007x24. Eyes symmetry, 0.93
75A direct way to find a relationship between the MOS and each of the 37 traits is using Pearson’scorrelation coefficient. We remind the reader that for two vectors, X = x 1 ,x 2 ,...,x n andY = y 1 ,y 2 ,...,y n , the Pearson’s correlation coefficient is given byr X,Y = cov(X,Y)σ X σ Y= E[(X −µ X)(Y −µ Y )]σ X σ Y, (6.1)where σ X and σ Y are being the standard deviations for X and Y , respectively. The coefficientranges between −1 and 1, with the two extreme points being obtained when the variables aremaximally linearly related.Pearson’s correlation coefficients are calculated for all 37 vectors, each vector correspondingto a feature. Per feature, a 260-values X vector describes each feature for each one of the 260training images 1 . The 260–values vector Y describes each image related MOS rating. Table 6.1itemizes these coefficients in decreasing order of importance with respect to the absolute Pearson’scorrelation coefficient.6.4.2 Insight provided from empirical dataThe first notable result reveals the strong correlation between the best ranked traits and theMOS, which even exceeds a Pearson’s correlation coefficient of 0.5 for the trait ’ratio eyeheight/face-height’.Particularly in regard to an automatic MOS prediction image processing toolthese results are very encouraging. Further we observe that photo-quality features play a less significantrole than facial aesthetics, as expected, but they are not to be neglected, since they achieveanr 14,MOS = 0.168. Moreover we note that the high ranked traitsx 1 ,x 2 andx 4 , which representthe ratios (eye-height/face-height) and (head-width/head-height), and furthermore face shape, seeTable 6.1 are features corresponding strongly to person’s weight. This outcome brings to the forethe strong importance of low human weight for aesthetics. Furthermore it is worth noting thatTable 6.1 reveals the surprising fact among others, that non permanent traits place a pivotal rolein raising the MOS rating. Eye make-up, lipstick, glasses and hair-style are all among the top 11of the obtained ranking. These results hint the high modifiability of facial aesthetics perceptionby simple means like make-up or hair styling. The relevance of eye make-up had been previouslyobserved in [GKYG10]. Together with the different conclusions that one may draw from Table6.1, it also becomes apparent that different questions are raised, on the interconnectedness of thedifferent traits. This is addressed in Section 6.4.3. Finally we note that traits, such as x 1 , x 7 ,x 12 and x 13 directly comply with the well known babyfaceness hypothesis (see [bea11]), whichdescribes that childlike facial features in females increase attractiveness, such features include bigeyes, e.g. x 1 and a relative low location of facial elements, e.g. x 7 , x 12 and x 13 . One measureknown for increasing attractiveness, if equal to the golden ratio φ = 1.618, is x 16 .6.4.3 Interconnectedness of different traitsTo get a better understanding of the role of the different traits in raising theMOS, it is helpfulto study the inter-relationship between these traits. This is addressed in Table C.3 in the Appendix,which describes the correlation between selected traits. Due to lack of space we limit the correlationmatrix to just a group of the first six traits. Table C.3 can answer different questions such asfor example the validity of the conclusion in Table 6.1 on the importance of the make-up feature.In this case, the question arises whether it is truly the make-up that affects the MOS or whether1. For information on denotation of features and according X–values, please refer to the Appendix, Table C.2
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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
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 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: 73Figure 6.3: Example image of the
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 and 126: 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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