Evaluation Of Facial Attractiveness Research Articles

The influence of featural and configural changes on the attractiveness evaluation of one’s own face

ABSTRACT In the present study, featural changes and configural changes were manipulated to explore the influence of such changes on the attractiveness evaluation of participants' own faces and other faces. Thirty female and 30 male participants were asked to complete three tasks, i.e., similarity rating of a given face to the original face, paired comparison of facial attractiveness, and subjective evaluation of facial attractiveness, on the same-sex original faces, featural faces and configural faces. The results revealed that the rank order of the attractiveness ratings of the three types of faces was generally identical: original faces > featural faces > configural faces, for both self-faces and other faces in male and female participants. More importantly, faces with configural changes were rated as less attractive than faces with featural changes, which is consistent with the configurational hypothesis. These findings have important implications for facial beautification.

A quantitative method for the assessment of facial attractiveness based on transfer learning with fine-grained image classification

In this paper, we investigate a new approach based on a combination of three-dimensional (3D) facial images and deep transfer learning (TL) with fine-grained image classification (FGIC) for quantitative evaluation of facial attractiveness. The 3D facial surface images of patients with and without filtering and the publicly available SCUT-FBP5500 dataset was used for transfer training and model pre-training, respectively. Experimental results show that a bilinear CNN model with a Gaussian filter freezing 80 % of the weights exhibit the strongest performance and lowest average error as a deep learning prediction model; the model was subsequently adopted for automatic assessment of facial attractiveness in clinical application. This is the first TL model with FGIC using 3D facial images for automatic quantitative evaluation of facial attractiveness in patients undergoing Orthognathic surgery (OGS). The developed web browser–based user interface enables effective and rapid assessment, thus contributing to effective patient–clinician communication and decision-making.

Objectively measured facial traits predict in-person evaluations of facial attractiveness and prosociality in speed-dating partners

Many studies have investigated whether facial averageness, masculinity, and similarity are associated with facial attractiveness. These studies have relied on ratings of images of real or digitally morphed faces. It is important to establish whether past findings translate to real-life, face-to-face evaluations of potential partners; lack of effects in this context would cast doubt on the evolutionary relevance of previous findings. Further, previous studies have not considered that, by definition, faces that are more similar to the average face (i.e. higher in averageness) tend to be more similar to raters' faces. Therefore, these image-rating studies, which separately found that averageness and (in some cases) similarity are attractive, are confounded. To address these issues, we conducted a laboratory-based speed-dating study of 682 participants with objectively measured facial traits, where opposite-sex pairs rated each other on facial attractiveness and prosociality. We found that facial attractiveness was predicted separately by averageness and by similarity (to the rater), but with both variables in the same model, neither uniquely predicted attractiveness. Similarity, but not averageness, predicted prosociality ratings. Facial masculinity was positively and negatively associated with facial attractiveness ratings of men and women, respectively. These results confirm, in real-life interactions, some key findings from image-rating studies but raise questions about others, notably the attractiveness of facial averageness.

The Effect of Moral Behavior on Facial Attractiveness.

Evaluating face attractiveness is a core aspect of face perception, which plays an important role in impression formation. A more reliable source of information in impression formation is moral behavior, which forms the primary basis for the comprehensive evaluation of others. Previous studies have found that one can easily form an association when faces and moral behaviors are presented together, which in turn affects facial attractiveness evaluation. However, little is known of the extent to which these learned associations affect facial attractiveness and whether the influence of moral behavior on facial attractiveness was related to facial appearance. We used the associative learning paradigm and manipulated face presentation duration (Experiment 1 and Experiment 2) and response deadline (Experiment 2) to investigate these issues. Under these conditions, the association information was difficult to be retrieved. Participants learned associations between faces and scenes of moral behavior, and then evaluated facial attractiveness. We found that both moral behavior and facial appearance influence facial attractiveness under conditions where associated information was difficult to retrieve, and their effects increased with the increase of face presentation time. With increasing response deadlines, the effect of moral behavior on facial attractiveness increased. The influence of moral behavior on facial attractiveness was associated with facial appearance. These results suggest that moral behavior continuously affects facial attractiveness. Our findings expand previous research by showing a robust influence of moral behavior on facial attractiveness evaluation, and highlight the important role of moral character in impression formation.

The influence of sex and rejuvenation conversion by the face editing app on facial attractiveness -Evaluation of facial attractiveness in images of others-

The influence of sex and rejuvenation conversion by the face editing app on facial attractiveness -Evaluation of facial attractiveness in images of others-

86. What Is Beauty? A Crowdsourced Evaluation of Facial Attractiveness

Purpose: Opinions of aesthetic outcomes can vary greatly amongst plastic surgeons. Our specialty creates changes for patients. Often, change can be mistaken for improvement. We are deemed experts of attractiveness, and thus it is not acceptable to have this degree of uncertainty on what is ideal. The authors hope to quantify cosmetic results to determine if postoperative changes are indeed better or worse than preoperatively. Evolutionary psychologists have shown that humans are koinophiles that seek out mates who possess average anatomy for the population. We particularly adore cognitive averages, meaning we subconsciously generate composites of the most recent attractive faces seen. Our study attempts to identify an “ideal” through crowdsourced ratings. We hypothesized a composite of the cohort would be most attractive and investigated which features correlated with attractiveness. Methods: Our male and female cohorts each consisted of 41 standardized frontal-view monochrome photos, with one composite image derived from the other forty. We measured 46 facial features per image, calibrating measurements with facial width-to-height ratio and interpupillary distance. We utilized Amazon Mechanical Turk, a widely used crowdsourcing platform, to receive ratings of images ranging from 1-7, with 1 and 7 being least and most attractive, respectively.Statistical AnalysisAnalysis of variance tests were performed for ratings and facial feature measurements. A Spearman correlation test calculated correlation coefficients between facial measurements and ratings. Lastly, we constructed heatmaps from facial measurements and scores. Results: 2064 evaluations revealed composite male and female scores (means were 5.31 and 5.60, respectively) were the highest of their cohorts and significantly greater (both p<0.0001) than scores of the other 40 photos (means were 4.06 and 4.00, respectively). Average ear width (r=1, p=0.0004) for males and average ear height, medial eye distance, and iris-to-aperture distance (all r=1, p=0.0004) for females had the highest correlations with attractiveness. Conclusion: Our study reports that average facial anatomy and ear features (heavily linked with chin-jaw and eye angles) are highly correlated with attractiveness. This is an initial step into the field of objective cosmetic quantifications and integrating evidence-based medicine into aesthetic surgery.

Comparative evaluation of facial attractiveness by laypersons in terms of facial proportions and equate it's deviation from divine proportions – A photographic study

IntroductionThe human face is a unique structure and says a lot about an individual even more than what the body indicates about the overall attractiveness. Facial attractiveness is an important key to social interactions and individual behavior. The human body is made in such a way that it follows a specific mathematical proportion called the Golden Proportion. In order for any two parts to be aesthetically proportionate, the ratio between them should be 1.618. Studies have shown that aesthetically pleasing profiles have facial proportions and ratios that closely resemble the golden proportions. AimTo evaluate the perception of esthetic facial photographs by laypersons and compare these with the accepted norms of divine proportions to find for any deviations from those norms. MethodologyForty subjects having normal soft tissue profile angle, as evaluated using Burstone's method, were selected to be photographed for the study. All the photographs were taken using DSLR Camera and under a standard photographic setup. All the photographs were edited and compiled into a PowerPoint presentation. The edited photographs on the PowerPoint presentation were displayed to 302 evaluators. These evaluators were asked to score each photograph according to a visual analogue scale with markings from 0 to 10 (with 0 being least attractive and 10 being most attractive) according to their attractiveness. All the scored photos were then grouped into three categories viz. Very attractive, Average attractive and Least attractive. Three horizontal proportions and three vertical proportions were assessed digitally in all the photographs and any deviations from the golden proportion were assessed. ResultsIt was found that none of the three groups followed golden proportions accurately. However the Very Attractive group showed closest resemblance to the golden proportion as compared to the other two groups. Amongst all the horizontal and vertical proportions in the Very Attractive group, Interchelion to Interalar proportion (Ch:Ln) was the one that showed maximum deviation from the divine proportion (mean value 1.38 ± 0.15). Rest of the other horizontal and vertical proportions in the Very Attractive group were close enough to the divine proportion. However the difference between the proportions amongst the three groups was not statistically significant. Comparison between the three groups showed that Very Attractive faces showed the least deviations from the golden proportions whereas least attractive faces showed most deviations from the golden proportions. ConclusionThe findings of this study state that the perception of aesthetically pleasing faces by laypersons do not accurately follow golden proportion but are close enough to it. The faces not showing golden proportions are perceived as less attractive.

Influence of incisor inclination and anterior vertical facial height on facial attractiveness in an adult Asian male

Balanced facial proportions and hard tissue relationships are important factors in facial attractiveness. The objective of this study was to determine the most pleasing maxillary incisal inclination (MII) on the lateral profile and the impact of various lower anterior facial height (LAFH) ratios (at rest and with a smile) on facial attractiveness. This cross-sectional study was performed with 66 raters equally divided into 3 panels: general dentists (GD), orthodontic residents (OR), and laypersons (LP). Lateral profile view photographs of an Asian man were modified using Photoshop software (Adobe Systems, San Jose, Calif) to show altered MII and LAFH ratios at rest and when smiling. Subjective evaluations of facial attractiveness were performed by raters using a visual analog scale. Kruskal-Wallis test was used to compare esthetic scores among raters, and the Mann-Whitney U test was applied to compare ES between groups. Kruskal-Wallis test showed significant differences in esthetic scores among raters for -10° (P=0.028) and -15° MII (P=0.030). Similarly, significant differences were found in ES for 8% LAFH ratio (P=0.022), and 4% LAFH ratio (P=0.035) at rest. Mann-Whitney U test showed significant differences between raters for -10° and -15° MII and between 8% and 4% LAFH ratio at rest. All groups rated -5° inclinations to be most attractive. General dentists and orthodontic residents found normal LAFH to be the most attractive, whereas long facial height was less attractive with smile. In addition, smile had a camouflaging effect on LAFH.

Identify Attractive and Unattractive Individuals Based on Geometric Features Using Neural Network

This paper aims to explore the essence of facial attractiveness from the viewpoint of geometric features toward the classification and identification of attractive and unattractive individuals. We present a simple but useful feature extraction for facial beauty classification. Evaluation of facial attractiveness was performed with different combinations of geometric facial features using the deep learning method. In this method, we focus on the geometry of a face and use actual faces for our analysis. The proposed method has been tested on, image database containing 60 images of men's faces (attractive or unattractive) ranging from 20-50 years old. The images are taken from both frontal and lateral position. In the next step, principle components analysis (PCA) was applied to feature a reduction of beauty, and finally, the neural network was used for judging whether the obtained analysis of various faces is attractive or not. The results show that one of the indexes in identifying facial attractiveness base of science, is the values of the geometric features in the face, changing facial parameters can change the face from unattractive to attractive and vice versa. The experimental results are based on 60 facial images, high accuracy of 88%, and Sensitivity of 92% is obtained for 2-level classification (attractive or not).

The influence of incisor inclination and anterior vertical facial height on facial attractiveness in an Asian woman

Orthodontic treatment can help improve facial attractiveness through the modification of factors affecting the soft tissue profile. The objectives of this study were to determine the impact of different maxillary incisal inclinations and lower anterior facial heights (at rest and with a smile) on the facial attractiveness of an Asian woman, as perceived by different panels of raters using visual analog scale (VAS) scores. A cross-sectional study was performed with 66 raters equally divided into 3 panels that consisted of general dentists, orthodontic residents, and laypersons. Raters assessed modified photographs of a subject with various incisor inclinations and lower anterior facial height/total anterior facial height (LAFH/TAFH) on lateral profile view. Modifications were made using Photoshop software (Adobe, San Jose, Calif). Subjective evaluations of facial attractiveness were performed by raters using VAS. Kruskal-Wallis test was used to compare VAS scores among raters. Mann-Whitney U test was applied to compare VAS scores between groups. Significant differences in VAS scores were found among raters for -10° (P=0.004) and -15° (P=0.021) incisal inclinations. Significant differences were found in VAS scores for -8% LAFH/TAFH (P=0.044) and 4% LAFH/TAFH with smile (P=0.002). Professionals preferred normal incisal inclinations to be the most attractive. General dentists found reduced facial height to be unattractive. Orthodontic residents and laypersons considered increased LAFH/TAFH to be most unattractive. Smile had a negative impact on VAS scores at extreme anterior facial height modifications.

Transfer Learning Adaptive Facial Attractiveness Assessment

Recent advances in Computer Vision and Artificial Intelligence have brought the opportunity to automate facial attractiveness evaluation. A range of studies have been addressed to the task and have achieved reasonable prediction accuracy. However, most of these methods work well only on photos with restrictions on expression, posture, illumination, but not on real-world face photos. This work is aimed to improve the attractiveness assessment state-of-the-art in both cases. To this end, an approach that employs transfer learning methodology as well as shallow machine learning was proposed for highly accurate facial attractiveness prediction. Specifically, a Convolutional Neural Network (CNN), Facenet, originally designed and pre-trained for the face recognition task is utilized. High-level facial features were extracted by using the network and then fed into Support Vector Regression in order to predict facial attractiveness. Extensive experiments conducted on widely used facial beauty datasets Gray and SCUT-FBP5500 demonstrated that the proposed method outperformed other attractiveness prediction approaches. The experimental results also confirmed the effectiveness of the method in both constrained and unconstrained environment.

A novel facial attractiveness evaluation system based on face shape, facial structure features and skin.

Facial attractiveness is an important research direction of genetic psychology and cognitive psychology, and its results are significant for the study of face evolution and human evolution. However, previous studies have not put forward a comprehensive evaluation system of facial attractiveness. Traditionally, the establishment of facial attractiveness evaluation system was based on facial geometric features, without facial skin features. In this paper, combined with big data analysis, evaluation of face in real society and literature research, we found that skin also have a significant impact on facial attractiveness, because skin could reflect age, wrinkles and healthful qualities, thus affected the human perception of facial attractiveness. Therefore, we propose a comprehensive and novel facial attractiveness evaluation system based on face shape structural features, facial structure features and skin texture feature. In order to apply face shape structural features to the evaluation of facial attractiveness, the classification of face shape is the first step. Face image dataset is divided according to face shape, and then facial structure features and skin texture features that represent facial attractiveness are extracted and fused. The machine learning algorithm with the best prediction performance is selected in the face shape structural subsets to predict facial attractiveness. Experimental results show that the facial attractiveness evaluation performance can be improved by the method based on classification of face shape and multi-features fusion, the facial attractiveness scores obtained by the proposed system correlates better with human ratings. Our evaluation system can help people project their cognition of facial attractiveness into artificial agents they interact with.

Roles of social knowledge and sexual dimorphism in the evaluation of facial attractiveness

Sexual dimorphism is an important feature of faces that can trigger trait inferences and affect the evaluation of facial attractiveness. However, face-based trait inference depends on adaptive cues that may be informative but are poor indicators of stable traits. This study involved two experiments to investigate the interaction between sexual dimorphism and social knowledge in relation to the evaluation of facial attractiveness. Experiment 1 examined the interaction between the valence of social knowledge and sexual dimorphism, and Experiment 2 examined the interaction between the content of social knowledge and sexual dimorphism. Results of Experiment 1 showed that irrespective of gender, positive social information significantly improved attractiveness ratings, while negative social information significantly reduced attractiveness ratings. Results of Experiment 2 showed that when combined with feminine features, warmth information significantly improved facial attractiveness judgments of both male and female faces. When combined with masculine features, on the other hand, for male faces, competence information could enhance facial attractiveness, while for female faces, warmth information improved facial attractiveness judgments. The above results showed that different dimensions of social information play different roles in the relationship between sexual dimorphism and facial attractiveness. This study deepens the discussion regarding the relationship between “physical beauty” and “moral worthiness”, suggests a dual path of top-down and bottom-up processing in the evaluation of facial attractiveness, and reminds governmental authorities that taking personal traits as the threshold to set up role models could avoid misleading teenagers.

Mother-Similar Faces Affecting Facial Attractiveness Evaluation and the Evidence from ERPs

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Facial attractiveness of cleft patients: a direct comparison between artificial-intelligence-based scoring and conventional rater groups.

To evaluate facial attractiveness of treated cleft patients and controls by artificial intelligence (AI) and to compare these results with panel ratings performed by laypeople, orthodontists, and oral surgeons. Frontal and profile images of 20 treated left-sided cleft patients (10 males, mean age: 20.5 years) and 10 controls (5 males, mean age: 22.1 years) were evaluated for facial attractiveness with dedicated convolutional neural networks trained on >17 million ratings for attractiveness and compared to the assessments of 15 laypeople, 14 orthodontists, and 10 oral surgeons performed on a visual analogue scale (n = 2323 scorings). AI evaluation of cleft patients (mean score: 4.75 ± 1.27) was comparable to human ratings (laypeople: 4.24 ± 0.81, orthodontists: 4.82 ± 0.94, oral surgeons: 4.74 ± 0.83) and was not statistically different (all Ps ≥ 0.19). Facial attractiveness of controls was rated significantly higher by humans than AI (all Ps ≤ 0.02), which yielded lower scores than in cleft subjects. Variance was considerably large in all human rating groups when considering cases separately, and especially accentuated in the assessment of cleft patients (coefficient of variance-laypeople: 38.73 ± 9.64, orthodontists: 32.56 ± 8.21, oral surgeons: 42.19 ± 9.80). AI-based results were comparable with the average scores of cleft patients seen in all three rating groups (with especially strong agreement to both professional panels) but overall lower for control cases. The variance observed in panel ratings revealed a large imprecision based on a problematic absence of unity. Current panel-based evaluations of facial attractiveness suffer from dispersion-related issues and remain practically unavailable for patients. AI could become a helpful tool to describe facial attractiveness, but the present results indicate that important adjustments are needed on AI models, to improve the interpretation of the impact of cleft features on facial attractiveness.

Visual attention during the evaluation of facial attractiveness is influenced by facial angles and smile.

To examine the changes in visual attention influenced by facial angles and smile during the evaluation of facial attractiveness. Thirty-three young adults were asked to rate the overall facial attractiveness (task 1 and 3) or to select the most attractive face (task 2) by looking at multiple panel stimuli consisting of 0°, 15°, 30°, 45°, 60°, and 90° rotated facial photos with or without a smile for three model face photos and a self-photo (self-face). Eye gaze and fixation time (FT) were monitored by the eye-tracking device during the performance. Participants were asked to fill out a subjective questionnaire asking, "Which face was primarily looked at when evaluating facial attractiveness?" When rating the overall facial attractiveness (task 1) for model faces, FT was highest for the 0° face and lowest for the 90° face regardless of the smile ( P < .01). However, when the most attractive face was to be selected (task 2), the FT of the 0° face decreased, while it significantly increased for the 45° face ( P < .001). When facial attractiveness was evaluated with the simplified panels combined with facial angles and smile (task 3), the FT of the 0° smiling face was the highest ( P < .01). While most participants reported that they looked mainly at the 0° smiling face when rating facial attractiveness, visual attention was broadly distributed within facial angles. Laterally rotated faces and presence of a smile highly influence visual attention during the evaluation of facial esthetics.

Beauty is in the eye of the beholder: The halo effect and generalization effect in the facial attractiveness evaluation

Beauty is in the eye of the beholder: The halo effect and generalization effect in the facial attractiveness evaluation

Evaluation of facial attractiveness in black people according to the subjective facial analysis criteria.

ABSTRACTObjective: The objective of this study was to evaluate the facial attractiveness in 30 black individuals, according to the Subjective Facial Analysis criteria. Methods: Frontal and profile view photographs of 30 black individuals were evaluated for facial attractiveness and classified as esthetically unpleasant, acceptable, or pleasant by 50 evaluators: the 30 individuals from the sample, 10 orthodontists, and 10 laymen. Besides assessing the facial attractiveness, the evaluators had to identify the structures responsible for the classification as unpleasant and pleasant. Intraexaminer agreement was assessed by using Spearman's correlation, correlation within each category using Kendall concordance coefficient, and correlation between the 3 categories using chi-square test and proportions.Results: Most of the frontal (53. 5%) and profile view (54. 9%) photographs were classified as esthetically acceptable. The structures most identified as esthetically unpleasant were the mouth, lips, and face, in the frontal view; and nose and chin in the profile view. The structures most identified as esthetically pleasant were harmony, face, and mouth, in the frontal view; and harmony and nose in the profile view. The ratings by the examiners in the sample and laymen groups showed statistically significant correlation in both views. The orthodontists agreed with the laymen on the evaluation of the frontal view and disagreed on profile view, especially regarding whether the images were esthetically unpleasant or acceptable.Conclusions: Based on these results, the evaluation of facial attractiveness according to the Subjective Facial Analysis criteria proved to be applicable and to have a subjective influence; therefore, it is suggested that the patient's opinion regarding the facial esthetics should be considered in orthodontic treatmentplanning.

Asymmetric Effect of Expression Intensity on Evaluations of Facial Attractiveness

Many studies have shown that facial expression influences evaluations of attractiveness, but the effect of expression intensity remains unclear. In the present study, participants rated the expression intensity and attractiveness of faces with happy, neutral, or sad expressions. Sad faces, as anticipated, were judged as less attractive than neutral and happy faces. Among happy expressions, faces with more intense expressions were considered more attractive; for sad expressions, there was no significant relationship between rating and intensity. Multiple regression analyses further demonstrated that the attractiveness of a face with a sad expression could be predicted only by its baseline attractiveness (i.e., ratings of neutral expressions). We conclude that the intensity of positive and negative expressions asymmetrically influences evaluations of the attractiveness of a face. We discuss the results in terms of emotional contagion or sympathy.

Role of Sagittal and Oblique Smiling Profiles in Evaluating Facial Esthetics

Surgeons and orthodontists used to use a conventional set of facial photographs, composed of front, front smiling, and profile images to evaluate facial esthetics, whereas sagittal and oblique smiling profile images have been largely neglected in practice. The aim of this study was to explore the importance of sagittal and oblique smiling profiles in evaluating facial esthetics. Photographs from 80 patients, of whom 40 underwent orthognathic surgery and 40 underwent orthodontic treatment, including front, front smiling, profile, sagittal profile smiling, and oblique profile smiling images before and after treatment, were collected and synthesized into 6 categories. Thirty judges gave scores to these photographs based on their own esthetic conception with a 1-week interval for each category. The results demonstrated that the mean score change of evaluating facial attractiveness of patients who underwent orthognathic surgery was lower when adding sagittal or oblique smiling profiles before the treatment, whereas it was higher after the treatment, which were opposite to the orthodontic treatment group with a higher score before the treatment and a lower score after the treatment when sagittal or oblique smiling profiles were added. The changes have a significant difference in adding both sagittal smiling profiles (P < 0.05) and oblique smiling profiles (P < 0.05) before and after treatment. Along with oblique smiling profile, sagittal smiling profile is crucial in evaluating facial esthetics for orthodontic treatment and orthognathic surgery. Both of them suggested to be integrated in routine photographic assessment of facial attractiveness evaluation before and after treatment, especially in orthognathic surgery for facial esthetic evaluation.