The Influence of Each Facial Feature on How We Perceive and Interpret Human Faces (2024)

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Iperception. 2020 Sep-Oct; 11(5): 2041669520961123.

Published online 2020 Sep 30. doi:10.1177/2041669520961123

PMCID: PMC7533946

PMID: 33062242

Jose A. Diego-Mas, Felix Fuentes-Hurtado, Valery Naranjo, and Mariano Alcañiz

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Associated Data

Supplementary Materials

Abstract

Facial information is processed by our brain in such a way that we immediately make judgments about, for example, attractiveness or masculinity or interpret personality traits or moods of other people. The appearance of each facial feature has an effect on our perception of facial traits. This research addresses the problem of measuring the size of these effects for five facial features (eyes, eyebrows, nose, mouth, and jaw). Our proposal is a mixed feature-based and image-based approach that allows judgments to be made on complete real faces in the categorization tasks, more than on synthetic, noisy, or partial faces that can influence the assessment. Each facial feature of the faces is automatically classified considering their global appearance using principal component analysis. Using this procedure, we establish a reduced set of relevant specific attributes (each one describing a complete facial feature) to characterize faces. In this way, a more direct link can be established between perceived facial traits and what people intuitively consider an eye, an eyebrow, a nose, a mouth, or a jaw. A set of 92 male faces were classified using this procedure, and the results were related to their scores in 15 perceived facial traits. We show that the relevant features greatly depend on what we are trying to judge. Globally, the eyes have the greatest effect. However, other facial features are more relevant for some judgments like the mouth for happiness and femininity or the nose for dominance.

Keywords: face perception, facial features appearance, facial traits, face judgments

Humans have highly developed their ability to perceive faces and to process the information extracted from them (; Damasio, 1985). The fusiform face area (Kanwisher etal., 1997) and the posterior superior temporal sulcus (Schobert etal., 2018) are a specialized neural network of our brains able to identify people; guess their gender, age, or race; or even, judge the emotions and intentions of the owners of the faces. Through this behavioral capacity to perceive faces, we use the facial appearance to make attributions such as personality, intelligence, or trustworthiness (). Therefore, faces affect our everyday decisions (Little etal., 2007; Todorov, 2011; Todorov etal., 2008; Zebrowitz & Montepare, 2008) such as mate selection (Bovet etal., 2012; Dixson etal., 2016; ), voting decisions (Little etal., 2007; Todorov etal., 2005), criminal justice decisions (Eberhardt etal., 2006; ), or how social partners are chosen (Langlois etal., 2000). Due to the importance of appearance-driven judgments of faces, face perception has become a major focus not only for psychological research but also for neuroscientists, engineers, and software developers ().

Visual perception research has shown that the human brain processes faces in a different way to other kind of objects (). Part-based perceptual models suppose that objects are processed on the basis of their components or parts (Biederman, 1987); although it is commonly agreed that this is the way in which we process most objects, faces are thought to be processed in a different way. In relational () or configural (Bartlett etal., 2003) models of perception, basic face features are processed in a part-based way, however, the perception relies heavily on the variations in the positioning of and the spacing between these basic features. Holistic perceptual models integrate facial features into a gestalt whole when the human brain processes facial information (holistic face processing; ; Young etal., 1987). The holistic models do not exclude part-based processing from the global holistic face perception process (; Rossion, 2008), and some part of the perception relies on part-based processing of faces.

The main objective of this work was to measure the effect size of each basic facial feature (e.g., two specifics eyes, a particular nose, a mouth, etc.) on the opinion of the observers about some of the whole face traits. Hereinafter, we will consider a face trait as any judgment that an observer can make about the physical characteristics of the face (e.g., attractiveness, masculinity/femininity, etc.) or about the emotional state of the face owner (e.g., sadness, happiness, fear, etc.). It is important to remark that we are considering each facial feature as a whole. For example, in this study, we consider the global appearance of the noses more than specific characteristics like dimensions or shapes.

Our secondary objective was to obtain models that predict the facial traits of faces from the combination of facial features that conform them. There are interactions between the facial features during face recognition tasks. However, regarding the facial traits assessment, although interactions between the features also exist, a more direct relationship with specific facial features can be established. For example, larger eyes, higher eyebrows, and smaller noses are perceived as baby-faced, and faces with some of these features are also perceived as baby-faced (; Zebrowitz & Montepare, 2008). A comprehensive discussion on this approach can be found in Zebrowitz-McArthur and Baron (1983). Although how the traits of a face are perceived depends on the whole face, the individual effect of each feature can explain part of the variation within the appraisals of the faces (; Rakover, 2002). Accordingly, some studies have used additive models of the facial attributes appraisals which explains most of the feasible explained variance (Gill, 2017; ). Other studies have related individual facial features to perceptions of the targets’ personalities (Paunonen etal., 1999) or have predicted facial trait evaluations from facial features accurately (Rojas etal., 2011). Obviously, unexplained variation must remain due to the interaction between the features under consideration and because the facial features included in the models do not cover the whole face.

Method

Selection of Faces, Facial Traits and Facial Features

To examine the influence of the appearance of each facial feature on the facial traits, we need a set of faces assessed by several observers with respect to the facial traits to be analyzed. On the other hand, the features of the faces must be classified by the similitude of their appearances.

Our faces express emotions by changing the shape of their features. Observers can judge if the observed person’s current emotional state is happy, angry, or sad based on these changes. For example, the owner of a smiling face looks happy but bored or tired if the face is yawning. Regardless of the expression, people make social trait inferences based on the facial appearance of faces in a neutral state. These inferences are not related to an instantaneous emotional state, although they are driven in part by their structural resemblance to emotional expressions (Petrican etal., 2014; Said etal., 2009). In this way, a neutral face can elicit in the observer sensations such as happiness, sadness, or dominance. The face’s owners can seem to be happy people although they are not smiling or laughing. In this work, we are interested in these facial traits that are not related to the instantaneous emotional state of the faces. For this reason, we used only neutral faces in our study, without expressions or deformations of the facial features.

Therefore, our first step was to obtain a set of photographs of faces with neutral expressions. After reviewing several well-known databases (Chihaoui etal., 2016), we selected the Chicago Face Database (Ma etal., 2015). This database contains high-resolution standardized images of real faces. The faces of the database belong to people between the ages of 18 and 40 living in the Chicago (U.S.A) area. All the images in the database have the same size and resolution; faces have the same position, pose, and orientation, and the background and illumination are uniform. The hom*ogeneity of the conditions in which the images were obtained was an important factor to select this face database because, for example, differences in the contrast of the image (Russell, 2003) or pose (Åsli etal., 2017; Favelle etal., 2011) can affect the way in which a face is perceived. For this study, we selected the subset of 93 photographs of White males with neutral expressions.

Using the Chicago Face Database supposes another advantage for our study. Each photograph is accompanied by information about the target face, and it has been rated by 74 participants on average (this number was calculated with the information available in Ma etal., 2015) on several facial traits: Afraid, Angry, Attractive, Baby-faced, Disgusted, Dominant, Feminine, Happy, Masculine, Prototypic, Sad, Surprised, Threatening, Trustworthy, and Unusual. Participants responded on a 1 to 7 Likert-type scale (1 = not at all, 7 = extremely) except for Prototypic, that was responded to on a 1 to 5 Likert-type scale. Prototypic was defined as to which degree the face seems typical. The raters showed a high degree of agreement in their assessments of the targets in all the traits. Detailed information on the database generation and characteristics of the participants is available in Ma etal. (2015). The mean scores of each facial trait for each face in our subset of 93 photographs are shown in Table S1 in Supplementary Material.

The facial features analyzed in this work were selected considering previous studies. Internal features (i.e., eyes, nose and mouth) seem to have significant importance in face recognition (Keil, 2009; Kwart etal., 2012). Among the internal features, eyes play a key role in face information processing (). Some authors include the eyebrows in the eye area (Saavedra etal., 2013; Sadr etal., 2003) or consider the eyebrows as a major factor in the perception of a face (Lundqvist etal., 1999). Blais etal. (2012) found that the mouth area is an important cue for both static and dynamic facial expressions, which was consistent with previous researches (Terry, 1977). However, external facial features such as hair or the shapes of the cheek, the chin, or the jaw also play an important role in the way in which the brain process the face information. According to Axelrod and Yovel (2010), the fusiform face area of the brain is not only sensitive to external features but also sensitive to their influence on the representation of internal facial features. Some works found that the face shape contributes significantly to face discrimination (Logan etal., 2017; Yamaguchi etal., 2013). From these previous works, we decided to consider the internal facial features (eyebrows, eyes, nose, and mouth) and the jaw contour in this study. Although other features have effects on face perception, for example, hair and facial hair, skin tone, and facial proportions (Dixson etal., 2016; Fink etal., 2006; Hagiwara etal., 2012; Jones etal., 2004; Pallett etal., 2010; ), we limited our study to those features that have a main effect on face perception, rather than considering features that may vary from time to time such as hair (people can get a haircut).

Classification of Facial Features by Appearance

To classify a big set of facial features is a complex task for humans. Classifying by appearance a very big set of elements in an undefined number of groups easily overwhelms our capacities for information processing (Miller, 1956; Scharff etal., 2011), and algorithms are more consistent for this task. Moreover, our brain integrates facial features into a gestalt whole when it processes a face’s information (; Young etal., 1987), decreasing our ability for processing individual traits or parts of faces (Taubert etal., 2011). On the other hand, individual differences exist in face recognition ability (Wang etal., 2012) and some matters, like the race of the face, influence the performance in processing features and the configuration of facial information (Hayward etal., 2008; Rhodes etal., 2009). This is reflected in low interobserver and intraobserver agreement in the evaluation of facial features (Ritz-Timme etal., 2011). To deal with these problems, we propose an automatic procedure to perform this task.

In a previous work (Fuentes-Hurtado etal., 2019), we developed an algorithm to automatically process images from the database and to extract individual images of the facial features of each face. Our objective was to extract the internal features (eyebrows, eyes, nose, and mouth) and the jaw contour. The RGB full-face photographs were used as the input of the algorithm for facial features extraction. The facial landmarks of each feature were detected, and the features separately extracted in images of the same size for each feature. This was accomplished using the CHEHRA facial key-point detector (Asthana etal., 2014). In this way, a set of landmarks was obtained for each photograph and, based on these landmarks, a mask for each feature was automatically created. The masks were used to extract the part of the image corresponding to each facial feature. The features were aligned with respect to the centroid of the previously acquired landmarks and saved as individual files. Figure 1 shows the set of eyes obtained using this procedure on our set of 93 photographs of White males with neutral expressions.

The Influence of Each Facial Feature on How We Perceive and Interpret Human Faces (2)

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Figure 1.

Left and Right Eye Images Obtained From 93 Photographs of White Males With Neutral Expressions. Right eyes were horizontally mirrored.

Figure 2 shows the set of mouths extracted from the database. As can be seen, the background of the images of the mouths is black. This is because the presence of hair around the mouth of men is common. In our first tests, we detected that the presence of hair greatly affected the process of grouping the mouths; therefore, we decided to remove the surroundings of the original mouth obtaining a shaved mouth. The procedure followed to shave the mouths was as follows: First, the outer landmarks of the mouth were selected to form a polygon. Then, this polygon was enlarged by five pixels in every direction to ensure the whole mouth was taken inside the mask. Finally, a Gaussian Blur Filter (sigma = two pixels) was applied to the mask in order to smooth the transition between the skin and the black background of the image.

The Influence of Each Facial Feature on How We Perceive and Interpret Human Faces (3)

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Figure 2.

Design

A general linear model (Searle, 1983) was fitted for each facial trait. Each model had five fixed factors (eyebrow, eye, nose, mouth, and jaw) and one of the 15 facial traits as dependent variable. The values of the dependent variables were the average ratings provided by human subjects (Ma etal., 2015). Our available data set consists of 93 observations with missing factors combinations. The 15 models were built considering only the main effects, without interactions. Some observations with abnormal studentized residuals were considered outliers and removed from the models. Depending on the facial trait analyzed, between zero and six faces were removed from the sample (marked with an asterisk in Table S1 in Supplementary Materials). The data were analyzed for each facial trait using an analysis of variance (ANOVA) with eyebrow (EB1–EB10), eye (E1–E16), nose (N1–N12), mouth (M1–M9), and jaw (J1–J11) as the main factors. The IBM SPSS Statistics 23.0 and Statgraphics Centurion v.XVII.II programs were used. The significance level was set at .05 for all tests.

Results

The detailed results of each analysis for the effect of facial features on facial traits can be consulted in Table S3 in Supplementary Materials. The overall ANOVA results were statistically significant for all facial traits except Disgusted, F(53, 39) = 1.163, p = .313. It is not possible to reject the null hypothesis that all the data come from groups that have identical means for Disgusted. Because of this, and as no significant effects of facial features were found on this facial trait, Disgusted will be excluded from the results and conclusions detailed hereafter. For the remaining 14 traits, R² varied in the range of .738 (Happy) to .898 (Prototypic), with adjusted R² values of .340 and .744, respectively. The fitted models (predicted vs. observed values) are shown in Figure S1 in Supplementary Materials. Individual tests found different statistically significant main effects by trait (Table 1 shows the significant effects with bold characters). The effect size of each facial feature on the dependent variable was measured by means of partial eta squared (η_p²). In this case, η_p² describes the proportion of total variance in each facial trait attributable to each facial feature. The complete set of values of η_p² is shown in the ANOVA tables in Supplementary Materials (Table S3), and the effect size of each facial feature by facial trait is shown in Table 1.

Table 1.

Effect Size (η_p²) and Statistical Significance of the Effects of Facial Features on Facial Traits.

	Eyebrow	Eye	Nose	Mouth	Jaw
Afraid
Effect size	η_p² = 0.42	η_p² = 0.70	η_p² = 0.59	η_p² = 0.51	η_p² = 0.55
Significance	F(9,34) = 2.717 p = .017	F(14,34) = 5.675 p < .0001	F(11,34) = 4.520 p < .0001	F(8,34) = 4.375 p = .001	F(10,34) = 4.144 p = .001
Angry
Effect size	η_p² = 0.36	η_p² = 0.62	η_p² = 0.24	η_p² = 0.53	η_p² = 0.54
Significance	F(9,35) = 2.182 p = .048	F(15,35) = 3.779 p = .001	F(11,35) = 0.988 p = .475	F(8,35) = 4.994 p < .0001	F(10,35) = 4.102 p = .001
Attractive
Effect size	η_p² = 0.52	η_p² = 0.62	η_p² = 0.53	η_p² = 0.48	η_p² = 0.49
Significance	F(9,34) = 4.140 p = .001	F(14,34) = 3.918 p = .001	F(11,34) = 3.484 p = .002	F(1834) = 3.842 p = .003	F(10,34) = 3.288 p = .005
Baby-faced
Effect size	η_p² = 0.29	η_p² = 0.54	η_p² = 0.37	η_p² = 0.34	η_p² = 0.37
Significance	F(9,36) = 1.631 p = .143	F(15,36) = 2.816 p = .006	F(11,36) = 1.892 p = .074	F(8,36) = 2.358 p = .038	F(10,36) = 2.132 p = .047
Disgusted
Effect size	η_p² = 0.19	η_p² = 0.41	η_p² = 0.17	η_p² = 0.24	η_p² = 0.29
Significance	F(9,39) = 1.028 p = .435	F(15,39) = 1.782 p = .074	F(11,39) = 0.689 p = .732	F(8,39) = 1.517 p = .183	F(1,39) = 1.576 p = .150
Dominant
Effect size	η_p² = 0.42	η_p² = 0.42	η_p² = 0.43	η_p² = 0.33	η_p² = 0.42
Significance	F(9,34) = 2.704 p = .017	F(14,34) = 1.784 p = .084	F(11,34) = 2.364 p = .027	F(8,34) = 2.064 p = .068	F(01,34) = 2.409 p = .027
Feminine
Effect size	η_p² = 0.24	η_p² = 0.44	η_p² = 0.43	η_p² = 0.51	η_p² = 0.43
Significance	F(9,34) = 1.189 p = .333	F(15,34) = 1.788 p = .079	F(11,34) = 2.354 p = .028	F(8,34) = 4.336 p = .001	F(10,34) = 2.560 p = .020
Happy
Effect size	η_p² = 0.26	η_p² = 0.24	η_p² = 0.17	η_p² = 0.47	η_p² = 0.44
Significance	F(9,35) = 1.381 p = .234	F(15,35) = 0.716 p = .752	F(11,35) = 0.644 p = .779	F(8,35) = 3.912 p = .002	F(10,35) = 2.734 p = .013
Masculine
Effect size	η_p² = 0.33	η_p² = 0.64	η_p² = 0.27	η_p² = 0.35	η_p² = 0.38
Significance	F(9, 34) = 1.840 p = .097	F(15,34) = 3.997 p < .0001	F(11, 34) =1.162 p = .348	F(8,34) = 2.307 p = .043	F(10,34) =2.087 p = .054
Prototypic
Effect size	η_p² = 0.62	η_p² = 0.70	η_p² = 0.39	η_p² = 0.39	η_p² = 0.43
Significance	F(9,35) = 6.297 p < .0001	F(15,35) = 5.313 p < .0001	F(11,35) = 2.043 p = .054	F(8,35) = 2.848 p = .015	F(10,35) = 2.621 p = .017
Sad
Effect size	η_p² = 0.39	η_p² = 0.49	η_p² = 0.46	η_p² = 0.39	η_p² = 0.44
Significance	F(9,36) = 2.576 p = .021	F(15,36) = 2.289 p = .021	F(11,36) = 2.827 p = .009	F(8,36) = 2.828 p = .015	F(10,36) = 2.787 p = .012
Surprised
Effect size	η_p² = 0.37	η_p² = 0.45	η_p² = 0.41	η_p² = 0.31	η_p² = 0.45
Significance	F(9,38) = 2.476 p = .025	F(15,38) = 2.060 p = .036	F(11,38) = 2.350 p = .025	F(8,38) = 2.141 p = .055	F(10,38) = 3.062 p = .006
Threatening
Effect size	η_p² = 0.52	η_p² = 0.57	η_p² = 0.38	η_p² = 0.39	η_p² = 0.52
Significance	F(9,36) = 4.263 p = .001	F(14,36) = 3.393 p = .002	F(11,36) = 2.010 p = .057	F(8,36) = 2.821 p = .015	F(10,36) = 3.822 p = .001
Trustworthy
Effect size	η_p² = 0.54	η_p² = 0.60	η_p² = 0.51	η_p² = 0.49	η_p² = 0.45
Significance	F(9,34) = 4.436 p = .001	F(15,34) = 3.358 p = .002	F(11,34) = 3.270 p = .004	F(8,34) = 4.033 p = .002	F(10,34) = 2.742 p = .014
Unusual
Effect size	η_p² = 0.39	η_p² = 0.57	η_p² = 0.41	η_p² = 0.33	η_p² = 0.40
Significance	F(9,36) = 2.553 p = .022	F(15,36) = 3.142 p = .002	F(11,36) = 2.270 p = .032	F(8,36) = 2.162 p = .055	F(10,36) = 2.406 p = .026

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Note. Significant effects are in boldface.

η_p² is usually preferred for effect size measurements in ANOVA (Richardson, 2011). However, to represent the effects size as percentages, another statistic, eta squared (η²), can be used for effect size measurements (). η² was calculated for each facial feature as the sums of squares for the feature divided by the total sums of squares for all effects and errors in the corresponding ANOVA study. Figure 3 shows the effect size of each facial feature on each facial trait as a percentage of all the effects, including the effect of the model error. Figure S2 in Supplementary Materials shows the effects on each facial trait by facial feature.

The Influence of Each Facial Feature on How We Perceive and Interpret Human Faces (4)

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Figure 3.

Effect Size of Each Facial Feature on Observed Facial Traits as a Percentage of All the Effects. Grayed area of bars and grayed cells represent variance unaccounted by the model (U.V.). Significant effects in green cells.

To check if the assumptions for ANOVA were met, the residuals of each adjusted model were tested for normality and heteroscedasticity. We assumed that each observation was independent from all other observations. Each face in the sample belongs to a different person randomly selected. In Figure S1 in Supplementary Materials, the scatterplots of residuals on predicted values of each model suggest that errors have constant variance, with the residuals scattered randomly around zero. The plots of normal probability of the residuals (Figure S1) suggest evidence of normality. Supplementary Kolmogorov–Smirnov tests were performed and skewness and kurtosis checked (α = .05). The results in the Table S2 show that null hypothesis cannot be rejected and conclude normality is a reasonable assumption for the residuals of all the facial traits.

Discussion

The main objective of this work was to measure how much the appearance of the facial features affects the opinion of the observers about some facial traits. We have proposed a new approach to relate 5 facial features of White males with 15 perceived facial traits. First, we selected 93 faces of White males with 15 facial traits assessed by human observers. Then, the facial features of the faces were categorized by appearance. To achieve this, we used an automatic procedure to extract the facial features from the whole face images, and PCA and K-means clustering algorithm were applied to characterize and group the facial features by their global appearance. Features belonging to the same category or cluster have a similar appearance, and it is supposed to have the same effect size on the face traits. In this way, our sample of 93 whole faces were classified by the clusters to which their features belong. Finally, general linear models relating the facial features of the faces to the observed facial traits were fitted for each trait. The effect size of each facial feature on the perceived facial traits was measured using the partial eta squared.

Classic behavioral work has shown that the human brain integrates facial features into a gestalt whole when it processes facial information (; Young etal., 1987), diminishing our capacity for processing facial information of faces with missing features or parts (Taubert etal., 2011). Therefore, information from all the facial features is used and plays a significant role in judging facial traits. Globally, our results are compatible with this, showing that most of the facial features have a statistically significant effect on how an observer perceives facial traits. In accordance with Cohen (1988), all of them can be considered large effects (η_p² > 0.1379).

However, the size of the effect varies for each facial feature. Our results show that eyes is the facial feature whose appearance has the biggest effect on perceived facial traits. The average percentage of effect on all the facial traits is 23.83%. Particularly, the effect of the eyes is over 30% on Masculine, Prototypic, and Afraid, being under 15% only on Happy (8.67%). The appearance of the jaw has the second mean effect size (15.73%), being the most hom*ogenous among the different traits analyzed. This result is consistent with previous studies that found that face shape contributes significantly to face processing (Logan etal., 2017; Yamaguchi etal., 2013). The mouth has a big effect on Happy, Feminine, and Angry (over 19%). This feature is related with dynamic facial expressions (Blais etal., 2012) and expresses emotions of happiness (e.g., smiling or laughing) and anger (e.g., screaming or shouting out) changing its shape. Therefore, the obtained results seems to be consistent with preceding works that found that people make trait inferences driven by structural resemblance to emotional expressions, even in neutral faces (Petrican etal., 2014; Said etal., 2009). On the other hand, our results show that jaw also has great effects on Happy and Angry. In the same way as in the case of the mouth, this could be related to the changes in the shape of the jaw when the face expresses happiness or anger, but this must be studied further.

The appearance of the eyebrows mean effect size is 13.11% being its most significant effect on Prototypic (23.34%). Therefore, eyebrows are an important clue to decide to which degree a face is the typical face of a White man. Lundqvist etal. (1999) found that the eyebrows are the most important feature for conveying facial threat. In the same way, our results show that eyebrows plays an important role in Threatening (18.74%). However, we found that eyes has more effect on Threatening (23.19%) than eyebrows. The difference in the results for the eyes may be due to the fact that they used schematic faces in their study and we have employed real faces. Nose has the smallest average effect (12.87%) on perceived facial traits. Its major effects are on Afraid, Sad, Dominant, Attractive, Feminine, and Trustworthy and are almost negligible on Happy and Angry.

In this study, η² and η_p² have been used to measure the effect size of each facial feature on each social trait. We have used both because η² is more intuitive to measure the effect sizes of the variables than η_p², but the former makes it hard to compare the effect of a single variable in different studies, and the proportion explained by any one variable depends on the number of variables on the model.

The obtained models were statistically significant (except Disgusted model) with R² varying in the range of .738 to .898. Therefore, most of the variation in each facial trait can be explained by the different class of the analyzed facial features. However, unexplained variation remains. Our results show higher unexplained variance in the models for Happy, Baby-faced, Surprised, or Dominant than those models for Attractive, Prototypic, or Afraid. It can be argued that the amount of unexplained variation in each model depends on the differences between the processes to assess each facial trait and the capacity of the models to capture the inherent variance of each kind of assessment. Ours models only consider the main effect of each independent variable, which may affect the capacity of the models to capture the variance to a different extent for each facial trait. On the other hand, faces with neutral expressions were assessed to create the models. This can lead to more unexplained variance in the models of the facial traits more related to facial expressions. For example, the differences between neutral and happy faces are larger than that between neutral and fearful faces (), making it more difficult to assess the happy versus fearful trait of a neutral face. Finally, our procedure does not consider the whole face space. Each facial trait may be linked to facial features not included in this study (such as hair or skin tone) to a different degree, leading to differences in the amount of unexplained variance between models.

As far as we know, this is the most comprehensive work, in terms of number of traits considered, measuring the effect size of the appearance of the facial features on the perception of facial traits. Other techniques, such us bubbles () or reverse correlation methods (; Todorov etal., 2011), can discriminate the features used by observers in categorization tasks. Our technique differs from these in several aspects. Our approach rests on the assumption that the global appearance of the facial features can be a good predictor of how features are used to assess the traits of the face. If the clustering procedure achieves enough intraclass hom*ogeneity and interclass heterogeneity, facial features belonging to the same class (with similar overall appearance) will lead to similar assessments, and these judgments will be dissimilar from those of faces with facial features belonging to other clusters. Our models use five meaningful independent variables each one describing a complete facial feature, rather than a collection of smaller features that conform faces (lines, shadows, surfaces . . . ) or disconnected areas of the faces. Therefore, a more direct link can be established between perceived facial traits and what people intuitively consider an eye, an eyebrow, a nose, a mouth, and a jaw.

On the other hand, our technique allows judgments to be made on complete real faces. Some other approaches involve using graphically manipulated photographs in the categorization tasks, by superimposing noise or hiding parts of the faces. In these approaches, judgments are made on synthetic, noisy, or partial faces that can influence the assessment.

However, some limitations of this study must be pointed out, mainly regarding the interactions between the facial features. In this work, we are measuring the main effect sizes. The main effect of each feature can explain part of the variation within the face appraisals (; Rakover, 2002). To consider the second-order effects (the effect of one feature’s appearance considering the other features’ appearance), a larger sample of images of real faces is needed. However, it is very difficult to develop a big enough face database and to collect ratings for all the faces. Moreover, it is not possible to find real faces with all the possible combinations of facial features. A usual way to achieve this is to collect ratings for faces in which specific areas of the face are parametrically manipulated in terms of size or shape. However, changing some relevant dimension of the facial features is not appropriate for this case because the classifications of facial features are based on their overall appearance.

On the other hand, we have followed a mixed feature-based/image-based approach to obtain the effect sizes of the appearance of the features. We have established a reduced set of relevant specific attributes to describe a face (five basic facial features) and used an image-based approach (PCA) to categorize these facial features by their global appearance. Therefore, although the faces were described by five meaningful variables, the configural information of the faces is not explicitly considered to obtain the effect size of the facial features.

Regarding the generalization of the findings, 93 faces of the Chicago Face Database were used to obtain the models relating facial features to facial trait assessments. The faces of the database belong to White men between the ages of 18 and 40 living in the Chicago (U.S.A) area. The subjective classifications of the faces were made by a specific group of women and men probably from the same city (Ma etal., 2015). Therefore, both the faces and the appraisals used to develop the models come from a specific community. The generalization of the results to faces of people from other communities must be carefully addressed. Especially, the findings cannot be generalized to faces of people of other races (Hayward etal., 2008; Rhodes etal., 2009) or to faces of people outside the range of this study.

Our future works will try to develop similar studies for female faces and to extend the results to other races. On the other hand, using a larger face database would allow us to consider interactions, at least of the second order, among the facial features, or to include more facial features in the models.

Supplemental Material

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Supplemental material, sj-pdf-1-ipe-10.1177_2041669520961123 for The Influence of Each Facial Feature on How We Perceive and Interpret Human Faces by Jose A. Diego-Mas, Felix Fuentes-Hurtado, Valery Naranjo and Mariano Alcañiz in i-Perception

Acknowledgements

This study was carried out using the Chicago Face Database developed at the University of Chicago by Debbie S. Ma, Joshua Correll, and Bernd Wittenbrink.

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

ORCID iD

Jose A. Diego-Mas https://orcid.org/0000-0002-3698-3411

Supplemental Material

Supplemental material for this article is available online at: http://journals.sagepub.com/doi/suppl/10.1177/2041669520961123.

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How to cite this article

Diego-Mas J. A., Fuentes-Hurtado F., Naranjo V., Alcañiz M. (2020). The influence of each facial feature on how we perceive and interpret human faces. i-Perception, 11(5), 1–18. 10.1177/2041669520961123 [PMC free article] [PubMed] [CrossRef]

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The Influence of Each Facial Feature on How We Perceive and Interpret Human Faces (2024)

FAQs

What do facial features tell about a person? ›

Facial information is processed by our brain in such a way that we immediately make judgments about, for example, attractiveness or masculinity or interpret personality traits or moods of other people. The appearance of each facial feature has an effect on our perception of facial traits.

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What is the study of facial features characteristics and their relation to human behavior? ›

physiognomy, the study of the systematic correspondence of psychological characteristics to facial features or body structure.

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What is the significance of facial features? ›

Attractive facial features play a significant role in creating a favorable impression and boosting confidence in social interactions. While various facial features contribute to attractiveness, some key ones include lips, teeth, skin, hair, cheekbones, nose, face shape, eyebrows, eyes, and jawline.

What does perception of facial features mean? ›

Face perception concerns the ability to detect a face in a visual scene (face detection), to discriminate a particular face from other faces (face individualization or individual face discrimination), and to associate a percept with a stored representation of a particular face in memory (face recognition).

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Does face shape determine personality? ›

Findings strongly support that the facial images of both men and women can predict their personality traits. An increasing number of studies have linked personality traits with face shape.

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Does a person's appearance reveal their personality? ›

Because of this, it can be said that physical appearance affects the environment that in turn affects personality. Much information already exists on such topics as how physical appearance affects happiness, self-esteem, and success.

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What facial features make someone look trustworthy? ›

People are more comfortable trusting someone whose face resembles a happy, approachable expression. Notice how the face on the left has upturned lips and raised eyebrows, like a smile. Conversely, the face on the right has downturned lips and harsh, threatening eyebrows, like a scowl.

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How can you tell human behavior by face? ›

The eyes are often described as "windows to the soul," and we often look to them to determine what someone else may be feeling. The eyes might be: Blinking quickly (meaning distress or discomfort) or blinking too little (which may mean that a person is trying to control their eyes)9.

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What influences facial features? ›

Over time, facial morphology across populations has been influenced by various factors, such as migration, mate-choice, survival and climate, which have contributed to variation in facial phenotypes. Genetic and facial phenotype data can be used to improve understanding of human history.

Can you tell a person's character from their face? ›

For example, there is evidence that character can influence facial appearance. Also, facial characteristics influences first impressions of others, which influences our expectations and behavior, which in turn influences character.

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What facial features indicate intelligence? ›

Faces that are perceived as highly intelligent are rather prolonged with a broader distance between the eyes, a larger nose, a slight upturn to the corners of the mouth, and a sharper, pointing, less rounded chin.

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Why is it important for humans to recognize faces? ›

The perception of facial features is an important part of social cognition. Information gathered from the face helps people understand each other's identity, what they are thinking and feeling, anticipate their actions, recognize their emotions, build connections, and communicate through body language.

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Why is facial appearance so important? ›

Faces that are less attractive, less average, less symmetrical, older, or less prototypical for their sex, create impressions of lower social competence, social power, sexual responsiveness, intelligence, and/or poorer health as well as more negative social outcomes.

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How does appearance affect perceptions of one another? ›

Physical appearance is often a major part of the halo effect. People who are considered attractive tend to be rated higher on other positive traits as well. However, this effect doesn't just affect our perceptions of people based on their attractiveness. It can also encompass other traits as well.

What facial features make you look rich or poor? ›

Each of these facial features also made faces appear more incompetent, cold, or untrustworthy. In contrast, faces perceived as rich were narrower and longer with upturned mouths and lighter, warmer complexions – features which corresponded to those associated with perceptions of competence, warmth, and trustworthiness.

Can you tell your ethnicity by facial features? ›

Ancestry and physical appearance are highly related; it is often possible to infer an individual's recent ancestry based on physically observable features such as facial structure and skin color.

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