FACIAL SOFT BIOMETRICS - Library of Ph.D. Theses | EURASIP
FACIAL SOFT BIOMETRICS - Library of Ph.D. Theses | EURASIP FACIAL SOFT BIOMETRICS - Library of Ph.D. Theses | EURASIP
72 6. SOFT BIOMETRICS FOR QUANTIFYING AND PREDICTING FACIAL AESTHETICSthe notion of attractiveness. The relevance and robustness of the provided ratings was confirmedin an experiment in [LLA + 08], where subjects re-rated a collection of images. For increasingrobustness, we consider only images that have received a sufficiently high number of ratings,specifically more than 70 ratings. We will henceforth refer to these ratings as the Mean OpinionScore (MOS). Among the chosen images of the database the mean MOS was 7.9, the standarddeviation was1.4, whereas the minimum value was4.3 and the maximum value was 9.9.The JPEG images in our database are of different resolutions and of different qualities.We now proceed with the description of the two groups of considered features: the photographaesthetics(image properties), and the facial aesthetics. All characteristics, from both groups, arestated in Table 6.1.6.3.2 Photograph aestheticsThe considered photograph aesthetic features are here specifically chosen to be simple andobjective. Firstly we include characteristics such as image resolution, image format (portrait orlandscape) and illumination. Furthermore, we consider the relative angle of the face in the photograph(this angle is denoted as α in Fig. 6.3). We also incorporate the zoom-factor, specificallyhow large the face appears in comparison to the image height. Finally we also connect threenovel image quality traits with facial aesthetics, which in previous work have been associated tophotograph-aesthetics: the relative foreground position, the BIQI and the JPEG quality measure.Regarding the relative foreground position, we essentially compute if the distance of the foreground’scenter of mass, (left eye, right eye or nose tip, respectively, see [cul]) to one of the stresspoints (see [GPJ04]) is shorter than to the center of the image. For clarity Figure 6.3 illustratesthe stress points of the image, where each of the four stress points is in a distance of 1/3 rd theimage width and 1/3 rd the image height from the boundary of the image, an aspect derived fromthe "Rule of thirds". In case that the foreground’s center of mass is equidistant to all stress points,which is the case in the image center, it has been shown that subjects lose their attention andinterest.The BIQI measure is based on the distorted image statistics and it employs support vectormachines for classification (see [MB09b] and [MB09a]). It is a blind quality measure; specificallyit is a no-reference assessment measure on image quality.The JPEG quality measure on the other hand considers artifacts caused by JPEG compression,such as blockiness and blurriness, evaluating again a no-reference score per image [WSB02].6.3.3 Facial characteristicsLiterature related to facial beauty (see [bea11]) identifies pertinent traits including the size ofthe face, the location and size of facial features like eyes, nose, and mouth, brows, lashes and lids,facial proportions, as well as the condition of the skin. Such literature confirms the role of thesefacial features in affecting human perception of beauty (see [bea11] and [BL10]). Drawing fromthis previous work, we also consider ratios of facial features and/or their locations by relating amultitude of measures, specifically including known facial beauty ratios adhering to the goldenratio, e.g. x 16 (see Table 6.1 for notations).Moreover we proceed a step further and consider soft biometric characteristics, such as eye-,hair- and skin-color, face- and brows-shape, as well as presence of glasses, make-up style and hairstyle.The full set of facial features is listed in Table 6.1 and can be categorized in the following fivegroups:
73Figure 6.3: Example image of the web site HOTorNOT, MOS = 9.8. The white disks representthe stress points, the red cross the image center.– Ratios of facial features and their locations,– Facial color traits,– Shapes of face and facial features,– Non permanent traits, and– Expression.Features related to the mouth and nose width were not exploited, due to the variety of expressionswithin the database. This expression variety causes significant diversity in the measurementsof both, mouth and nose. All selected traits are listed in Table 6.1 (the photograph aesthetics areadcebhgifFigure 6.4: Example image of the web site HOTorNOT, MOS = 9.2 with employed facial measures.highlighted for a better overview). Table C.1 and Table C.2 in the Appendix exhibit the traits,trait instances and furthermore the range of magnitude for all photograph aesthetics and facial
- 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
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- Page 37 and 38: 3.5.2 Analysis of interference patt
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- Page 41 and 42: 39Table 3.4: Example for a heuristi
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- 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: 71raphy considerations include [BSS
- 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
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- 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
73Figure 6.3: Example image <strong>of</strong> the web site HOTorNOT, MOS = 9.8. The white disks representthe stress points, the red cross the image center.– Ratios <strong>of</strong> facial features and their locations,– Facial color traits,– Shapes <strong>of</strong> face and facial features,– Non permanent traits, and– Expression.Features related to the mouth and nose width were not exploited, due to the variety <strong>of</strong> expressionswithin the database. This expression variety causes significant diversity in the measurements<strong>of</strong> both, mouth and nose. All selected traits are listed in Table 6.1 (the photograph aesthetics areadcebhgifFigure 6.4: Example image <strong>of</strong> the web site HOTorNOT, MOS = 9.2 with employed facial measures.highlighted for a better overview). Table C.1 and Table C.2 in the Appendix exhibit the traits,trait instances and furthermore the range <strong>of</strong> magnitude for all photograph aesthetics and facial
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