FACIAL SOFT BIOMETRICS - Library of Ph.D. Theses | EURASIP
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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

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70 6. SOFT BIOMETRICS FOR QUANTIFYING AND PREDICTING FACIAL AESTHETICSFigure 6.1: Golden ratio applied in a human face.fuller lips, long and dark eyelashes, high cheek bones and a small nose [bea11]. An overview ofpsychological studies based on face proportions and symmetries is presented in [BL10].Finally the babyfaceness or cute faces elicit sympathy and protective urges. Traits in babyfacesinclude a big round forehead, low located eyes and mouth, big round eyes and small chin, seeFigure 6.2.Figure 6.2: Babyfacesness theorem: females with child like traits are perceived sympathetic.6.1.2 Beauty and image processingFrom an image processing point of view, few attempts seek to exploit and validate some ofthe aforementioned psychological results and even introduce early methods for beauty prediction.In [GKYG10] for example, the authors present a multi-layer neuronal network for beauty learningand prediction regarding faces without landmarks. Such approaches often accept interesting applications,as the automatic forecast of beauty after a plastic surgery in [GPJ04]. The same work dealswith beauty classification, considering facial measurements and ratios, such as ratios of distancesfrom pupil to chin and from nose to lips (see also the work in [AHME01] and [MJD09]).6.1.3 Photo–quality and aestheticsBroad background work on image quality assessment (IQA) is needed in applications suchas image transmission, lossy compression, restoration and enhancement. The subjective criteriaintertwined with image quality are assessed in numerous metrics for mobile phones, electronictabs or cameras. A number of automatic IQA algorithms has been built on those metrics. For anoverview of related works, see [WBSS04] and [SSB06].From a photographic point of view, the presence of people, their facial expressions, imagesharpness, contrast, colorfulness and composition are factors which play a pivotal role in subjectiveperception and accordingly evaluation of an image, see [SEL00]. Recent works on photog-

71raphy considerations include [BSS10] and [CBNT10]. Hereby the authors reveal that appealingphotographs draw from single appealing image regions as well as their location and the authorsuse this proposition to automatically enhance photo-quality. Photo-quality can be also influencedby image composition, see [OSHO10]. Finally there are current studies, which model aestheticperception of videos [MOO10]. Such methods have become increasingly relevant due to the prevalenceof low price consumer electronic products.6.2 ContributionIn this chapter we study the role of objective measures in modeling the way humans perceivefacial images. In establishing the results, we incorporate a new broad spectrum of known aestheticalfacial characteristics, as well as consider the role of basic image properties and photographaesthetics. This allows us to draw different conclusions on the intertwined roles of facial featuresin defining the aesthetics in female head-and-shoulder-images, as well as allows for further insighton how aesthetics can be influenced by careful modifications.Towards further quantifying such insights, we construct a basic linear metric that models therole of selected traits in affecting the way humans perceive such images. This model applies as astep towards an automatic and holistic prediction of facial aesthetics in images.The study provides quantitative insight on how basic measures can be used to improve photographsfor CVs or for different social and dating websites. This helps create an objective viewon subjective efforts by experts / journalists when retouching images. We use the gained objectiveview to examine facial aesthetics in terms of aging, facial surgery and a comparison of averagefemales relatively to selected females known for their beauty.The novelty in here lies mainly in two aspects. The first one is that we expand the pool of facialfeatures to include non permanent features such as make-up, presence of glasses, or hair-style. Thesecond novelty comes from the fact that we seek to combine the results of both research areas, thusto jointly study and understand the role of facial features and of image processing states.6.3 Study of aesthetics in facial photographsIn our study we consider 37 different characteristics that include facial proportions and traits,facial expressions, as well as image properties. All these characteristics are, manually or automaticallyextracted from a database of 325 facial images. The greater part of the database, 260images, is used for training purposes and further 65 images are tested for the related validation.Each image is associated with human ratings for attractiveness, as explained in Section 6.3.1. Thedatabase forms the empirical base for the further study on how different features and propertiesrelate to attractiveness.We proceed with the details of the database and related characteristics.6.3.1 DatabaseThe database consists of 325 randomly downloaded head-and-shoulders images from the website HOTorNOT [Hot11]. HOTorNOT has been previously used in image processing studies(see [GKYG10] [SBHJ10]), due to the sufficiently large library of images, and the related ratingsand demographic information.Each image depicts a young female subject (see for example Fig. 6.3 and Fig. 6.4.) and wasrated by a multitude of users of the web site. The rating, on a scale of one to ten, corresponds to

70 6. <strong>SOFT</strong> <strong>BIOMETRICS</strong> FOR QUANTIFYING AND PREDICTING <strong>FACIAL</strong> AESTHETICSFigure 6.1: Golden ratio applied in a human face.fuller lips, long and dark eyelashes, high cheek bones and a small nose [bea11]. An overview <strong>of</strong>psychological studies based on face proportions and symmetries is presented in [BL10].Finally the babyfaceness or cute faces elicit sympathy and protective urges. Traits in babyfacesinclude a big round forehead, low located eyes and mouth, big round eyes and small chin, seeFigure 6.2.Figure 6.2: Babyfacesness theorem: females with child like traits are perceived sympathetic.6.1.2 Beauty and image processingFrom an image processing point <strong>of</strong> view, few attempts seek to exploit and validate some <strong>of</strong>the aforementioned psychological results and even introduce early methods for beauty prediction.In [GKYG10] for example, the authors present a multi-layer neuronal network for beauty learningand prediction regarding faces without landmarks. Such approaches <strong>of</strong>ten accept interesting applications,as the automatic forecast <strong>of</strong> beauty after a plastic surgery in [GPJ04]. The same work dealswith beauty classification, considering facial measurements and ratios, such as ratios <strong>of</strong> distancesfrom pupil to chin and from nose to lips (see also the work in [AHME01] and [MJD09]).6.1.3 <strong>Ph</strong>oto–quality and aestheticsBroad background work on image quality assessment (IQA) is needed in applications suchas image transmission, lossy compression, restoration and enhancement. The subjective criteriaintertwined with image quality are assessed in numerous metrics for mobile phones, electronictabs or cameras. A number <strong>of</strong> automatic IQA algorithms has been built on those metrics. For anoverview <strong>of</strong> related works, see [WBSS04] and [SSB06].From a photographic point <strong>of</strong> view, the presence <strong>of</strong> people, their facial expressions, imagesharpness, contrast, colorfulness and composition are factors which play a pivotal role in subjectiveperception and accordingly evaluation <strong>of</strong> an image, see [SEL00]. Recent works on photog-

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