Perception of health from facial cues

Henderson, Audrey Joan; Holzleitner, Iris J; Talamas, Sean N.; Perrett, David I.

doi:10.1098/rstb.2015.0380

Cited by 96 publications

(105 citation statements)

References 65 publications

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“…In a similar attempt, Henderson and colleagues [38] estimated the mean difference in face shape between the ten individuals with the lowest BMI and the ten ones with the highest BMI, after a reduction of shape coordinates to a set of principal components. The resulting “BMI axis” and the projections of the individual faces onto this axis to estimate “facial BMI scores” correspond to our regression axes and the resulting shape scores.…”

Section: Discussionmentioning

confidence: 99%

BMI and WHR Are Reflected in Female Facial Shape and Texture: A Geometric Morphometric Image Analysis

et al. 2017

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Facial markers of body composition are frequently studied in evolutionary psychology and are important in computational and forensic face recognition. We assessed the association of body mass index (BMI) and waist-to-hip ratio (WHR) with facial shape and texture (color pattern) in a sample of young Middle European women by a combination of geometric morphometrics and image analysis. Faces of women with high BMI had a wider and rounder facial outline relative to the size of the eyes and lips, and relatively lower eyebrows. Furthermore, women with high BMI had a brighter and more reddish skin color than women with lower BMI. The same facial features were associated with WHR, even though BMI and WHR were only moderately correlated. Yet BMI was better predictable than WHR from facial attributes. After leave-one-out cross-validation, we were able to predict 25% of variation in BMI and 10% of variation in WHR by facial shape. Facial texture predicted only about 3–10% of variation in BMI and WHR. This indicates that facial shape primarily reflects total fat proportion, rather than the distribution of fat within the body. The association of reddish facial texture in high-BMI women may be mediated by increased blood pressure and superficial blood flow as well as diet. Our study elucidates how geometric morphometric image analysis serves to quantify the effect of biological factors such as BMI and WHR to facial shape and color, which in turn contributes to social perception.

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Section: Discussionmentioning

confidence: 99%

BMI and WHR Are Reflected in Female Facial Shape and Texture: A Geometric Morphometric Image Analysis

et al. 2017

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“…The shape characteristics associated with perceived health paralleled our results. In women, perceived health is associated with upward mouth curvature, but not with eyelid openness42.…”

Section: Discussionmentioning

confidence: 96%

“…In a sample of children and adolescents, three normalized distances representing the lower face area were enough to train a machine to predict body weight from facial portraits41. Our approach does not require preselection of subsamples or preselection of specific local features as in Henderson and colleagues42. When analyzed by shape regressions, such features and contrasts are implicitly embraced by a single pooled analysis of all the landmarks and semilandmarks on all the faces.…”

Section: Discussionmentioning

confidence: 99%

Patterns of correlation of facial shape with physiological measurements are more integrated than patterns of correlation with ratings

Windhager

Bookstein

Millesi

et al. 2017

Sci Rep

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This article exploits a method recently incorporated in the geometric morphometric toolkit that complements previous approaches to quantifying the facial features associated with specific body characteristics and trait attribution during social perception. The new method differentiates more globally encoded from more locally encoded information by a summary scaling dimension that is estimated by fitting a line to the plot of log bending energy against log variance explained, partial warp by partial warp, for some sample of varying shapes. In the present context these variances come from the regressions of shape on some exogenous cause or effect of form. We work an example involving data from male faces. Here the regression slopes are steepest, and the sums of explained variances over the uniform component, partial warp 1 and partial warp 2 are greatest, for the conventional body mass index, followed by cortisol and, lastly, perceived health. This suggests that physiological characteristics may be represented at larger scale (global patterns), whereas cues in perception are of smaller scale (local patterns). Such a polarity within psychomorphospace, the global versus the focal, now has a metric by which patterns of morphology can be modeled in both biological and psychological studies.

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“…A predisposition to reading faces may have significant evolutionary benefits. For example, we are able to predict whether someone is ill or not just by observing facial photographs (Axelsson et al., ), probably because of changes in skin colour and facial expression (Henderson, Holzleitner, Talamas, & Perrett, ). Judgements of ill health can subsequently lead to avoidance behaviours, hypothesised to slow the spread of disease (Park, Van Leeuwen, & Chochorelou, ).…”

Section: Introductionmentioning

confidence: 99%

The effect of sleep deprivation on objective and subjective measures of facial appearance

Holding

Sundelin

Cairns

et al. 2019

Journal of Sleep Research

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The faces of people who are sleep deprived are perceived by others as looking paler, less healthy and less attractive compared to when well rested. However, there is little research using objective measures to investigate sleep‐loss‐related changes in facial appearance. We aimed to assess the effects of sleep deprivation on skin colour, eye openness, mouth curvature and periorbital darkness using objective measures, as well as to replicate previous findings for subjective ratings. We also investigated the extent to which these facial features predicted ratings of fatigue by others and could be used to classify the sleep condition of the person. Subjects (n = 181) were randomised to one night of total sleep deprivation or a night of normal sleep (8–9 hr in bed). The following day facial photographs were taken and, in a subset (n = 141), skin colour was measured using spectrophotometry. A separate set of participants (n = 63) later rated the photographs in terms of health, paleness and fatigue. The photographs were also digitally analysed with respect to eye openness, mouth curvature and periorbital darkness. The results showed that neither sleep deprivation nor the subjects’ sleepiness was related to differences in any facial variable. Similarly, there was no difference in subjective ratings between the groups. Decreased skin yellowness, less eye openness, downward mouth curvature and periorbital darkness all predicted increased fatigue ratings by others. However, the combination of appearance variables could not be accurately used to classify sleep condition. These findings have implications for both face‐to‐face and computerised visual assessment of sleep loss and fatigue.

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Perception of health from facial cues

Cited by 96 publications

References 65 publications

BMI and WHR Are Reflected in Female Facial Shape and Texture: A Geometric Morphometric Image Analysis

BMI and WHR Are Reflected in Female Facial Shape and Texture: A Geometric Morphometric Image Analysis

Patterns of correlation of facial shape with physiological measurements are more integrated than patterns of correlation with ratings

The effect of sleep deprivation on objective and subjective measures of facial appearance

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