It's sometimes hard to pinpoint exactly what it is that attracts us to someone. It might be their confidence, or their sense of humour, or you might just like the way they look. A lot of research over the years has gone into trying to work out what it is that makes us fancy each other. Results have varied, showing women may like the smell of men who have a particular kind of diet , and men may find women in groups more attractive. Some research has suggested we often go for people who share some of the same characteristics we do.
New York Times. The relationship between weight and attractiveness is demographically variable; for example, American men of European descent rate lower weights as more attractive, except in extremely low BMI ranges [ 13 ], whereas African American men are more likely to prefer heavier figures [ 14 ]. Women may get a better offer by waiting until the man has more status and resources due to being older. From this, some have concluded that men perceive a more muscular male body to be ideal, as distinct from a woman's ideal male, which is less muscular than what men perceive to be ideal. Scientists are also learning that there may be a practical side to our obsession with beauty. Preparation of images All images were colour-calibrated using Attractiveness and facial features program Xnd 61 to control for subtle random variations in colour featjres to lighting Attractievness photographic conditions. It's not an absolute rule, though.
Tempering brass. 2. Asymmetry
Attractiveness and facial features Review. Journal of Theoretical Biology. And perhaps physical attractiveness isn't just some arbitrary superficial set of attributes that men can just overlook. Continue Reading. These findings made him rule out the possibility that a preference for women with proportionately longer legs than men is due proportionately longer legs being a secondary sex characteristic of women. Both men and women judge women with smaller waist-to-hip ratios more attractive. John Wiley and Sons. Including assumptions about a person's race, socioeconomic class, intelligence, and physical attractiveness. Although the thing is, that I tried another 5 different personal photos, Fetish webcam cum shot which I got 5 different scores from 8. The Washington Times. Furst Company.
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The attractive traits that have been implicated as signals of biological quality include sexual dimorphism, symmetry, averageness, adiposity, and carotenoid-based skin colour. In this study, we first provide a comprehensive examination of the traits that predict attractiveness. In men, attractiveness was predicted positively by masculinity, symmetry, averageness, and negatively by adiposity. In women, attractiveness was predicted positively by femininity and negatively by adiposity.
Skin colour did not predict attractiveness in either sex, suggesting that, despite recent interest in the literature, colour may play limited role in determining attractiveness.
Male perceived health was predicted positively by averageness, symmetry, and skin yellowness, and negatively by adiposity. Female perceived health was predicted by femininity. We then examined whether appearance predicted actual health using measures that have been theoretically linked to sexual selection, including immune function, oxidative stress, and semen quality. In women, there was little evidence that female appearance predicted health.
In men, we found support for the phenotype-linked fertility hypothesis that male masculinity signalled semen quality. However, we also found a negative relationship between averageness and semen quality.
Overall, these results indicate weak links between attractive facial traits and health. Human facial attractiveness has been studied widely in the context of biologically-based preferences 1 , 2 , 3. In contrast to the biological account, it has been suggested that our preferences are products of our cultural experiences 4.
This view has been challenged by two types of evidence. Not only is there cross-cultural agreement on what we consider attractive 5 , 6 , but also our facial preferences emerge at an early age 7 , 8 , before it is likely for cultural learning to have an influence on our preferences.
Moreover, facial attractiveness and attractiveness-related traits have been shown to be associated with mate choice and mating success in humans 9. Together, these results suggest that our facial preferences in part reflect adaptations that evolved via sexual selection.
Evolutionary theories propose that our preferences for certain traits evolved because those traits provide signals of biological quality, particularly physical health The immune system is the main physiological system for fending off disease-causing pathogens or parasites. Oxidative stress refers to the balance between the production of reactive oxygen species ROS that cause structural and DNA damage to cells and the efficiency of the antioxidant system to nullify the ROS Oxidative stress has been associated with diseases such as heart disease and cancer 15 , It is also linked to health functions such as semen quality and immune function due to the high concentration of polyunsaturated fats in sperm and immune cells, which make these cells highly susceptible to ROS damage 14 , Semen quality is important because sperm cells are critical for male fertility.
Direct benefits include disease avoidance, increased fertility, and material advantages, such as better nutrition, parenting, and protection. Indirect benefits include obtaining genes that code for offspring health.
Evolutionary psychologists have identified several facial traits as potential candidates for biologically-based mate preferences, including sexual dimorphism masculinity for men and femininity for women , averageness, symmetry, adiposity, and skin colour 1 , 2 , 3.
Sexual dimorphism, averageness, and symmetry, in particular, have been widely studied. A meta-analysis showed that these three traits are all significantly related to attractiveness 2. Adiposity has also been found to be related to attractiveness The association between skin colour, particularly carotenoid-based skin yellowness, and facial attractiveness has been gaining attention only in recent years. Carotenoids are red and yellow pigments that influence our skin colour when consumed through fruits and vegetables.
Inter- and intra-individual variation in fruit and vegetable intake correlates with skin yellowness 19 , Skin yellowness is related not only to facial attractiveness 21 , but also to other attractive traits such as sexual dimorphism In this study, we examine how all these traits are linked to attractiveness and health.
There are putative mechanistic links associating sexual dimorphism, averageness, symmetry, and skin colour with health. Male sexual dimorphism may signal health via the effects of testosterone Testosterone compromises health by suppressing immune function 24 and increasing oxidative stress Therefore, theories suggest that only healthy males can afford to sustain elevated levels of testosterone for the development of sexually dimorphic traits 23 , Testosterone is also crucial for spermatogenesis Therefore, masculinity may also signal semen quality.
Femininity has also been suggested to signal health in women via similar mechanisms with female hormones 2 , 27 , although there is some debate over the effect of female hormones on health Several mechanisms have been proposed as the link between carotenoid-based colouration and health. The carotenoid trade-off hypothesis proposed that using carotenoids for colouration prevents their use as antioxidants to quench ROS 12 , The hypothesis proposed that this trade-off leads to a significant relationship between colouration and health because healthy individuals can afford to devote more carotenoids to signalling.
The hypothesis, however, has been questioned as some studies have failed to demonstrate that carotenoids possess antioxidant properties 30 , with a recent study finding that oral supplementation with the carotenoid beta-carotene did not affect health in humans As an alternative to the carotenoid trade-off hypothesis, the carotenoid protection hypothesis proposed that the carotenoid-based colouration could also signal the presence of other antioxidants that protect carotenoids from being damaged by ROS and losing their colour as a result Supportive evidence comes from findings that supplementation of non-pigmentary antioxidants enhanced carotenoid-based colouration intensity in a number of species 32 , 33 , To sum up, there are reasons to expect each of the traits to be correlated with health.
Although sexual dimorphism, averageness, symmetry, adiposity, and skin colour have all been associated with attractiveness, they have generally been studied individually. The exceptions were two recent studies which found that skin colour, specifically yellowness and lightness, predicted attractiveness in men while masculinity did not 21 , We still do not know much about the contributions of the different traits to attractiveness because no studies have examined them together.
Little is also known about the relationship between facial appearance and actual health. In general, theories of sexual signalling predict that attractive appearance would be positively related to actual health 10 , 11 , Only a few studies have examined the relationship between facial appearance and measures of immune function or oxidative stress. Using a hepatitis B vaccination protocol, Rantala et al. Using an index of oxidative stress derived from measures of oxidative damage to the DNA and lipids, Gangestad et al.
These studies had only male participants, so it is also unclear whether the results hold for women. The few studies of the relationship between facial appearance and semen quality have reported mixed results.
Soler et al. In contrast, Peters, Rhodes, and Simmons 39 did not find any relationship between semen quality and facial attractiveness or attractive traits such as masculinity, symmetry, and averageness. Notably, no studies have examined the relationship between any actual health measures and skin colour. The aim of the present study is to provide a comprehensive assessment of the facial appearance predictors of attractiveness, perceived health, and actual health.
First, we investigate whether any of the facial traits, including sexual dimorphism, averageness, symmetry, adiposity, and skin colour, positively predict facial attractiveness. Second, we investigate whether any of the facial traits positively predict perceived health.
Lastly, we investigate whether any of the facial traits predict any of the actual health measures, including immune function, oxidative stress, and semen quality. With the exception of semen quality, we examine all relationships for both men and women. Zero-order correlations between the appearance and health variables are presented in Table 1. For each sex, we ran separate multiple regression analyses to examine the facial appearance variables that predicted attractiveness, perceived health, and each of the actual health variables.
We ran separate multiple regression models for colour and the other appearance traits. We did so because perception of sexual dimorphism, averageness, symmetry, and adiposity could be influenced by both face shape and colour.
Indeed, the zero-order correlations show that skin colour is related to traits such as male symmetry and female femininity Table 1. Therefore, analysing skin colour together with the other appearance traits in the same multiple regression models might lead us to underestimate the contribution of colour.
Age was included as a control variable in all regression models. Visual examination of the scatterplots indicated a potential curvilinear relationship between adiposity and attractiveness in women. Therefore, we also added the quadratic term of adiposity into the model for female attractiveness.
Descriptive statistics for the facial appearance variables are presented in Table 2. According to the multiple regression results Table 3 , male attractiveness was significantly and positively predicted by masculinity, symmetry and averageness and negatively by adiposity.
Female attractiveness was significantly and positively predicted by femininity and negatively by adiposity. Male perceived health was positively predicted by averageness, symmetry, and yellowness and negatively by adiposity Table 4. Female perceived health was positively predicted by femininity Table 4. Principal components analyses PCA were conducted to summarize the interrelated immune function variables. Both male and female data returned two PCs see supplementary material for details on data reduction.
For men, PC1 was loaded mainly by bacterial killing capacity and overall bacterial immunity and PC2 was loaded mainly by bacterial suppression capacity and lysozyme activity. For women, PC1 was loaded mainly by bacterial killing capacity, overall bacterial immunity and lysozyme activity and PC2 was loaded mainly by bacterial suppression capacity. No lifestyle factors were significantly related to either PC2 in women or any of the PCs in men. Therefore, we ran these analyses on the raw data.
Multiple regression results indicated that there were no significant appearance predictors of either immune PC in either men or women Tables 5 and 6. Raw descriptive statistics for the oxidative stress variables are presented in Tables 7 and 8. The isoprostane data was log-transformed to achieve normal distribution for the data analyses. The 8-OHdG analyses for women were based on residuals that were extracted after controlling for various lifestyle variables see supplementary material.
Lifestyle factors did not significantly affect isoprostane levels in women or either of the oxidative stress measures in men. Therefore, we ran the regression analyses on the raw data of these variables.
After doing distracting math problems, participants saw the pictures again, but with information about the person's personality. Lipstick Colors for Fair Skin. But beauty does not have set parameters. Furthermore, when photographs of faces in profile were used in which there is no symmetry between the front and back of the head the average of these photographs was consistently judged to be the most attractive. They might just be that pretty, but I still have my suspicions. Men and women had to judge photographs of women's faces taken during their fertile phase.
Attractiveness and facial features. Post Comment
Nothing attracts a man other than beautiful eyes, pair of blue, hazel or brown eyes are actually the killer, provides calm, comfort and warmth to the males. Thick limbal rings in eyes proved to be attractive for women because natural and protruding limbal ring profound a true sign of youth. Warm and inviting eyes always gather attraction and appreciation.
Moreover big eyes are the symbol of innocence and cuteness as team of physiologist in BBC proved a men are fascinated to large eyes a lot.
There are long and short faces, based on their longitudinal dimensions but oval ones are common in women that gives the feminine look, define delicacy and most of all inspires opposite sex. Women with symmetrical faces have more womanly features. Women with such facial features aids to drag an eye on men with perfect balance between the concave and convex areas of the face. Round face with chubby cheeks is somehow liked by men but it gives a childish touch. Moreover long and thin faces do not impress men and not considered as the desired facial features of fascination.
I must say that beauty lie in proportion but cheerful smile and good facial features nurtures the attractiveness of any face and creates an aura of elegance, making her tremendously beautiful. Why is a narrow nose better or more beautiful? Of course a white person wrote this article. Naturally they will take up for their own. It all depends on what type of narrow nose it is. Narrow noses can be long like Pinocchio, they also can have hooks on their narrow bridges like a witch.
Witches also have narrow noses and they are ugly. Wide noses are definitely cute too. Narrow is not better than wide, it all depends on what type of wide and narrow nose you have, fact.
This was a bit offensive to all the girls out there with hook noses and narrow noses that they are insecure about. Ive seen latin women with the same traits. The people with narrower nose look beautiful. I agree, I kind of have a big fat nose and it ruins my freaken face, Im a girl and I kind of more look like a man or just idk. I mean Im skinny but my face just looks horrible.
I dont have big eyes and I have dumb eyebrows that arch down and it drives me crazy. I think high cheek bones with a more narrow slimmer face always look more attractive than a round chubby face with low cheek bones. And before consideration of size of eyes, nose or lips. Their shape and soul is important and can be attractive or not. For example imagine a woman with eyes like fox and small nose but short and like a pig shape but small and small lips but very wide mouth!
Very very ugly and even horrible! And on the other hand imagine rihanna! Pointed Nose Tip. A narrow nose with a pointed tip is very alluring. What makes it more attractive, is not having those prominent wrinkle lines also known as smile lines , from the sides of your nostrils to the corners of your lips.
High Cheek Bones. Chubby cheeks may look cute, but they aren't very attractive. High cheek bones have always been a very sexy feature in women. Cheek bones should have a hollow curve, that is in line with your lips, slanting upwards to the start of your ears. Full Lips. Lips are a very important facial feature. Full lips look enticing and luscious.
A natural pout enhances the look. Your lower lip should be fatter than your upper lip. The outline of your lips should be a straight line and not a smudgy one.
A good teeth set and jawline play an important role in the positioning of your lips. Tapering Chin. A narrow, tapering chin always looks very charming and attractive. The chin should not be over-sized, nor should it be too small. A medium-sized chin is just perfect. After the jawline, the chin is the final feature that defines your face. Flawless Skin.
Flawless skin is the key to attractiveness. It gives your face a very neat and supple look. The skin tone also plays an important role here. Slightly tanned, golden brown skin has always been a big turn on. But otherwise, even if you are on the fairer or darker side, it hardly matters, as long as you have a flawless skin, which is devoid of marks, freckles and pimples. Defined Face Cut. If I were to go by majority, then a slightly oval face is what is liked by most people. Yes, round faces with chubby cheeks do look good on certain women, but then, these face types have a certain amount of childishness.
Long and thin faces, devoid of any face fat, are usually not among the most-desired of face types. Prominent Dimples. Dimples add a lot of beauty to your smile, making it infectious. Some of us may also have a dimple chin also known as cleft chin.
A mild disorder in reality, dimples have turned into a very likable facial feature. Additional Features in Women. Beauty spots like moles, when present in the right places, add class to your beautiful face. These beauty marks are sometimes falsely created by people, using everyday cosmetics.
The famous symbol of beauty, Marilyn Monroe, had a prominent mole on her face. Additional Features in Men. Facial features like forehead, eyes, cheeks, nose, and skin would be more or less the same, but the lips would not need that pout.
Men would need a stronger, angular, masculine, and a more prominent jawline and chin. They need not have curvy or arch-shaped brows either. Their brows should be thicker, as compared to a woman's. In men, most women prefer clean-shaven or the stubble look. A well-maintained goatee or French beard, is also considered attractive. Perfectly set features make a face attractive. Some Beauty Tips. For scanty eyebrows, use a brow pencil.
Just have one made with an eyeliner. They won't be visible, however, you'll have defined cheek bones. Use blush to enhance the look. But I always feel that looking beautiful is more about feeling beautiful from within. Cherish your beauty. Disclaimer: This article does not represent people of any particular race, color or ethnicity. Share This. Beauty Secrets for Face.
Beauty Tips for the Face.
Predictors of facial attractiveness and health in humans | Scientific Reports
Help us improve our products. Sign up to take part. A Nature Research Journal. The origin and meaning of facial beauty represent a longstanding puzzle.
Despite the profuse literature devoted to facial attractiveness, its very nature, its determinants and the nature of inter-person differences remain controversial issues. The results reveal that different subjects prefer distinguishable regions of the face-space, highlighting the essential subjectivity of the phenomenon.
The different sculpted facial vectors exhibit strong correlations among pairs of facial distances, characterising the underlying universality and complexity of the cognitive processes, and the relative relevance and robustness of the different facial distances.
The notions of body beauty and harmony of proportions have fascinated scholars for centuries. From the ancient Greek canons, a countless number of studies have focused on unfolding what is behind the beauty of the face and the body.
Nowadays the notion of facial beauty is a fast expanding field in many different disciplines including developmental psychology, evolutionary biology, sociology, cognitive science and neuroscience 1 , 2 , 3 , 4 , 5.
Still, despite a profuse and multi-disciplinary literature, questions like the very nature of facial attractiveness, its determinants, and the origin of inter-subject variability of aesthetic criteria, elude a satisfactory understanding.
The face is the part of the human body from which we infer the most information about others, such as: gender, identity, intentions, emotions, attractiveness, age, or ethnicity 6 , 7 , 8. In particular, looking at a face, we are able to immediately acquire a consistent impression of its attractiveness. Still, we could have a hard time explaining what makes a face attractive to us. As a matter of fact, which variables determine attractiveness and their interactions are still poorly understood issues 3.
According to this hypothesis, a face is judged on average as attractive according to a set of innate rules typical of the human species, which stand out with respect to other social or individual factors. Some degree of consensus has, indeed, been reported 9 , 10 , 11 , 12 , Most of these experiments are based on the measurement of correlations among numerical ratings assigned to a set of natural or synthetic 14 , 15 facial images by raters belonging to different cultural groups.
Much work in this field has also been devoted to assessing the covariation of the perceived beauty of a face with facial traits that are believed to signal good phenotypic condition, mainly: facial symmetry, averageness and secondary sexual traits. After decades of intense research, the role played by these traits is known to be limited: facial beauty seems to be more complex than symmetry 5 , averageness 14 , 16 and secondary sexual traits 7 , Indeed, it has been documented that cultural, between-person and intra-person differences influence attractiveness perception in various ways 4.
As a representative example, the link between masculinity and attractiveness in male faces is subject to significant inter- and intra-subject differences 4 , 5 , 7 , An evolutionary explanation is that exaggerated masculinity could be perceived as denoting a lack of some personality facets such as honesty or expressiveness In this context, the so called multiple fitness or multiple motive model 4 , 11 , 19 proposes that attractiveness varies according to a variety of motives , each one evoking a different abstract attribute of the person whose face is evaluated.
On the other hand, an impressive amount of work is committed to the automatic facial beauty rating. This is tackled as a supervised inference problem whose training database is composed of natural facial images codified by vectors of facial coordinates in face-space 3 , 20 , 21 , along with inter-subject averaged numerical ratings assigned to them by human subjects, to be inferred. Works differ mainly on the codification of faces in the face-space: from a geometric face description 2D or 3D spatial coordinates of the facial landmarks , to a detailed description of the texture or luminosity degrees of freedom that provide a cue to the facial shape in depth there also exist holistic representations, extracting lower-dimensional, non-local information from the facial image according to some criterion Principal Component eigenfaces or Gabor filters ; or using richer techniques which integrate geometric from skin textural and reflectivity characteristics.
With the advent of deep hierarchical neural networks, the raw facial image is given as an input to the algorithm, which automatically extracts the putative relevant features in the inference process, although in a hardly accessible way the black box problem. The supervised inference of ratings may help to address, albeit indirectly, the impact of various facial features on attractiveness.
Although the relative relevance of different features has been discussed in various articles, robust conclusions are lacking 3 , 22 , 23 , 24 , 25 , 26 , 27 , The results about the relative relevance of the kind geometric, textural and holistic of facial attributes to attractiveness are controversial as well 3 , 29 , 30 , 31 , 32 , In any case, the integration of different kinds of variables seems to improve the inference results 29 , 34 , suggesting that these are complementarily taken into account in the cognitive process of attractiveness assessment.
Facial beauty is, hence, probably not a universal function of a set of few facial properties, as implicitly assumed in many references, but the result of a complex process in which multiple semantic concepts, providing cues to personality facets, are inferred. The literature concerning inference of personality traits indicates that such semantic concepts may be encoded in global combinations of facial features, in a complex way This motivates a study of facial beauty beyond the subject-averaged rating, focusing on the inter-subject heterogeneity and on the global combinations of various facial features generating such a diversity.
In summary, the complexity of facial attractiveness perception so far prevented a satisfactory understanding of how attractiveness relates to various facial elements 3 , and of the nature of inter-personal differences. In order to make progress, from a methodological point of view it is important to highlight three key factors.
A The possible mutual influence among geometric, texture and detailed features Even considering the problem in terms of geometric variables only, the possible existence of interactions or mutual dependencies between different facial components may induce a variety of possible pleasant faces, even for the single subject.
B The undersampling of the relevant face-space, due to the many different prototypes of facial beauty 14 , C The subjectivity of the phenomenon, probably hindered by the use of the average numerical beauty ratings. The complexity and richness of the perceptual process, suggested by the multiple-motive hypothesis and by previous work about perception of personality dimensions 6 , 37 , 38 , 39 , eludes a description in terms of average ratings, a quantity that has already been observed to be inadequate 3.
In light of these considerations, we here address the phenomenon of facial preference through an empirical approach that aims at removing the biases of ratings, focusing instead on the possibility given to human subjects to freely explore a suitably defined face-space. By means of a dedicated software, based on image deformation and genetic algorithms, we focus on inter-subject differences in aesthetic criterion and let several subjects sculpt their favorite variation of a reference portrait , parametrized by a vector of geometric facial coordinates.
We observe how different subjects tend to systematically sculpt facial vectors in different regions of the face-space, which we call attractors , pointing towards a strong subjectivity in the perception of facial beauty. In addition, the facial vectors sculpted by different subjects exhibit strong correlations for pairs of facial distances, which is a manifestation of the underlying universality and complexity of the cognitive process of facial image discrimination.
The correlations contain information regarding the different sources of variability in the dataset of selected vectors. For instance, though a difference between male-female subjects is clearly observed, the largest differences among facial variations, elicited by a principal component analysis, result from criteria that are transversal with respect to the gender only. A third important result concerns the assessment of the robustness of the results with respect to the degrees of freedom not described in the face-space.
Crucially, in our approach, the luminance, texture and detailed degrees of freedom are decoupled from the geometric features defining the face-space, and deliberately kept fixed, and common for all the subjects.
Finally, we observe that the overall experimental results are, interestingly, partially robust and independent of the detailed degrees of freedom the reference portrait. The current experimental scheme bypasses the three confounding factors A—C mentioned in the precedent paragraph. A Uncontrolled sources of biases are absent in our study, since all possible facial variations given the reference portrait are described by points in the face-space. B In our face-space of reduced dimensionality and unchanged texture degrees of freedom the undersampling is mitigated, making possible an efficient exploration of the face space and allowing for an accurate characterisation of the single-subject attractor.
We consider a face-space defined by a set of geometric coordinates illustrated in Fig. The face-space vector components f i are, in this way, either landmark Cartesian coordinates or inter-landmark distances.
From a vector of facial coordinates f and a reference facial portrait corresponding to a real person, we then construct a facial image by a continuous deformation of the reference portrait such that its landmark geometric coordinates acquire the desired value, f Fig.
Within a single experiment, the reference portrait the image texture is unchanged and only the geometric position of the landmarks can change for an in-depth explanation see Sec. Methods and the Supplementary Information. A The parameters defining the face space. B Reference portrait RP1 used in experiment E1 along with its corresponding landmarks in blue. C Image deformation of RP1 according to a given vector of inter-landmark distances d : the blue reference portrait landmarks are shifted leading to the red points so that their inter-landmark distances are d , and the reference image B is consequently deformed.
D Image deformation of the reference portrait RP2 according to the same vector of distances d as in C. In the next subsection, we discuss the degree of reproducibility of our results as a function of N , T and S 1.
Intra-population distance of the populations sculpted by different subjects s as a function of the generation t. The Euclidean metrics in face space has been used see Supplementary Sec. S4 , although the results are qualitatively equal for other relevant metrics.
Each curve corresponds to a different subject for 10 randomly chosen subjects. The intra-population distance decreases with the generation index, indicating that the populations sculpted by single subjects tend to clusterize in a region of the face-space. This clustering is not observed in a null experiment in which the left-right decisions are taken randomly. Remarkably, a diversity of behaviors towards the pseudo-stationary regime is observed, already signaling differences in the way the face-space is explored.
In the next subsection we show that the face-space attractors of different subjects are actually significantly and consistently different. This experimental scheme is, therefore, able to resolve the subjective character of attractiveness, as the single subject tends to sculpt populations of vectors clustered in a narrow region in the face-space in successive realisations of the experiment.
The attractors are extrema of such a function in the sense that a significant fluctuation of a vector coordinate away from its value in the attractor will tend to lower its probability of being selected by the subject, given the reference portrait.
In order to assess the subjectivity of the sculpting process, we need to measure to what extent the same subject, by repeating the same experiment, would sculpt populations of facial vectors closer to each other than to populations sculpted by distinct subjects. If subjectivity was at play in the sculpting process, and not hindered by the stochasticity of the algorithm, the self-consistency distances would be lower than inter-subject distances.
This is clearly the case, see Fig. In Fig. The intra-population distances are not suitable for an assessment of the subject self-consistency, since they strongly depend on the number of generations performed by the genetic algorithm c. The emerging scenario is that of single subjects who, in a single realization of the sculpting experiment, end up in a very clustered population blue curve in Fig. Performing several realizations of the same experiment leads the subject to a slightly different population in face-space orange curve in Fig.
These self-consistent populations are anyway closer to each other than to populations sculpted by different subjects, as witnessed by the larger inter-subject distances , whose histogram is presented in the green curve in Fig. A crucial point is that the distance between the inter-subject green curve, i and self-consistency orange curve, sc histograms in Fig. S3 would be even larger in an experiment with a higher number of generations T.
Methods and Supplementary Sec. Furthermore, larger values of S 1 , S sc , m would give rise to a lower statistical error of the considered observables see Supplementary Sec. Main panel: Normalised histograms of pseudo-distances. Blue : subject intra-population distances , or self-distances of all the populations sculpted in E1.
Orange : self-consistency distances , or distances among couples of populations sculpted by the same subject in E2. Green : inter-subject distances , or distances among couples of populations sculpted by different subjects in E1. Purple : distances among couples of populations sculpted by different subjects in different experiments, E1 and E3 differing in the reference portrait. Red : distances among couples of populations sculpted by subjects of different gender in E1.
This quantity corresponds to 1. This result is compatible with previous work proposing that such face-space metrics is the one that best captures differences in facial identity 21 , Using the simple Euclidean metrics the Euclidean distance per coordinate in physical coordinates , the inter-subject and self-consistency distances result slightly more overlapping, although still clearly distinct.