Facial Beauty Research Articles

(Self-)Envy, Digital Technology, and Me

Using digital technology, in particular social media, is often associated with envy. Online, where there is a tendency for people to present themselves in their best light at their best moments, it can feel like we are unable to turn without being exposed to people living out their perfect lives, with their fancy achievements, their beautiful faces and families, their easy wit, and wide social circles. In this paper, I dive into the relationship between envy and digital technology. I offer an enriched account of envy that aims to establish both the situated nature of envy, as well as (more controversially) the possibility of self-envy. I explore how features of digital technology not only increase the frequency with which we might experience envy directed at others, but also envy directed at other versions of one’s self. For online, we not only encounter idealized versions of others but digitally idealized versions of our selves. Moreover, I argue that digital technology does not only increase the likelihood of experiencing self-envy but makes the experience less cognitively onerous. I conclude by considering how current digital technology shapes what we envy.

The Ideal Proportion of the Auricle Exposure via a Morphometric Analysis in Asian Women.

Ears are an important aesthetic feature that is vital to the overall attractiveness of the face. Although there have been many studies on the aesthetics of the auricle, there is currently a lack of consensus on the ideal proportion of auricle exposure for Asian women in frontal view. This study aimed to investigate ideal proportion of auricle exposure in Asian women. An observational study was carried out on the photographs of 84 women on the list of the 100 most beautiful faces in Asia (published by TCC Asia in 2020). The proportion of the distance between the outer canthus and the outermost point of auricle to the distance between the inner canthus and the outermost point of auricle was calculated as the auricle exposure proportion. Evaluators were asked to rank a set of photographs of the volunteer with varying auricle exposure proportions from most attractive to least attractive. Measurements of the photographs of the 84 women showed a mean ear exposure proportion of 0.600. With 487 questionnaire responses received, the proportion of auricle exposure that the evaluators considered most attractive was 0.600. People with aesthetic experience considered 0.625 the most attractive proportion, while the general group considered 0.600 the most attractive. The ideal proportion of the auricle exposure for Asian women is in the range of 0.60-0.625, which may help surgeons reconstruct aesthetically pleasing ears. This journal requires that authors assign a level of evidence to each article. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors www.springer.com/00266 .

Anthropometry of the nose pre- and post-photogrammetric adjustments in a sample of Syrian medical students. A cross-sectional study.

The individual's perceived beauty, sense of identity, and general well-being are all influenced by the social implications of the esthetics of their facial appearance. One of the major indicators of one's facial beauty is anthropometric proportions. This study was conducted to measure and evaluate the anthropometric proportions in a Syrian population, and to determine their desired nasal shape. A cross-sectional study was conducted on Syrian university students. Lateral and frontal pictures of the participants were taken using CanonEOS 250D from a standard distance of 100 cm. These pictures were analyzed, then nasal measurements were adjusted according to the participants' desire using Adobe Photoshop 2020. All anthropometric measurements were conducted on AutoCAD® 2019 software before and after the adjustments. Data was analyzed using the SPSS-25 using paired samples T test to compare the means. The study included 53 females and 47 males. The mean age was 22.25. The mean unadjusted nasofrontal angle, nasolabial angle, nasomental angle, nasofacial angle, nasal index, Byrd and Hobar ratio, and Powell-modified Baum ratiowere 141.80, 102.18, 129.21, 34.60, 71.71, 0.62, and 3.08 in the rhinoplasty-negative participants, and 145.89, 102.03, 130.20, 32.44, 67.96, 0.64, and 3.06 in the rhinoplasty-positive participants, respectively. While the mean of the adjusted previously mentioned angles were 144.59, 106.32, 131.19, 31.15, 62.91, 0.60, and 3.19 in the rhinoplasty-negative participants, and 146.31, 102, 130.20, 31, 62.55, 0.62, and 3.08 in the rhinoplasty-positive participants, respectively. In contrast to other populations, our study concluded that Syrians had unique anthropometric measurements and facial esthetics preferences. Moreover, the local plastic surgeons were able to identify most of the preferred nasal measurements for rhinoplasty in this ethnic group. It is advisable to use photogrammetry before rhinoplasty to identify the preferred nasal measurements of each individual to ensure the patient's satisfaction with the outcome of the surgery. Level IV, cross-sectional study.

REPRESENTASI SENSUALITAS PEREMPUAN DALAM IKLAN ZILINGO (STUDI SEMIOTIKA DENGAN CHARLES SANDERS PIERCE)

Between mass media and women likened two sides of coin that can’t be separated. Advertising describes women as a weak creature. The role of women that always describe in ads always describing domestic roles. Women also exploited by sensuality that they have like their body as a mean of attracting in mass media industry. This research is researching Zilingo version Classy or Glossy. the representation of women on this ad represented in the form dancing women, flirting with ex her boyfriend, by being exploited with their body. The technique that used ini ad by using jingle or interesting slogans. The research wants to know how Zilingo represented women’s sensuality by selecting sign like picture or other signs. The approach used in this research is semiotic theory from Charles Sanders Pierce known as a theory triangle meaning in analyzing the sign. The object of this research consisting of word, picture in Zilingo ad. The result shows sensual women, according to Zilingo, describe having slim stature, black hair, beautiful face and break the traditional style of women. Beside that, Zilingo ad also have a message as a picture frame and social frame. In a picture frame, women try to attract alluring. And in social image, women being active againstthe traditional concept of women. Of course, it is suitable with the main purpose of ad is to persuade consument in order to use their product, service in a form of creative advertising strategy.

Learning feature alignment across attribute domains for improving facial beauty prediction

Facial beauty prediction (FBP) aims to develop a system to assess facial attractiveness automatically. Through prior research and our own observations, it has become evident that attribute information, such as gender and race, is a key factor leading to the distribution discrepancy in the FBP data. Such distribution discrepancy hinders current conventional FBP models from generalizing effectively to unseen attribute domain data, thereby discounting further performance improvement. To address this problem, in this paper, we exploit the attribute information to guide the training of convolutional neural networks (CNNs), with the final purpose of implicit feature alignment across various attribute domain data. To this end, we introduce the attribute information into convolution layer and batch normalization (BN) layer, respectively, as they are the most crucial parts for representation learning in CNNs. Specifically, our method includes: 1) Attribute-guided convolution (AgConv) that dynamically updates convolutional filters based on attributes by parameter tuning or parameter rebirth; 2) Attribute-guided batch normalization (AgBN) is developed to compute the attribute-specific statistics through an attribute guided batch sampling strategy; 3) To benefit from both approaches, we construct an integrated framework by combining AgConv and AgBN to achieve a more thorough feature alignment across different attribute domains. Extensive qualitative and quantitative experiments have been conducted on the SCUT-FBP, SCUT-FBP5500 and HotOrNot benchmark datasets. The results show that AgConv significantly improves the attribute-guided representation learning capacity and AgBN provides more stable optimization. Owing to the combination of AgConv and AgBN, the proposed framework (Ag-Net) achieves further performance improvement and is superior to other state-of-the-art approaches for FBP.

Female power awakening—My grandmother enlights me with her personal experience

In the eyes of many young girls, Barbie is the symbol of the perfect woman. She is like an excellent mirror, not only showing the not real female image, but also reflecting the deep aesthetic gaze of the traditional gender discipline of the society behind the current female's crazy pursuit of beauty -- having a slim figure and a beautiful face is the embodiment of the greatest value of women. Many women believe that external elements, such as beauty and sex appeal, are a way for them to build close relationships and seek social approval, thus fuelling anxiety about their body shape and appearance. When I was younger, I had the same idea that short hair made a woman less of a woman, but my grandmother, a district attorney, took action to break that stereotype. Starting from her, I also see the emergence of various female forces in the society. They do not limit their lives to the pursuit of external beauty, but carry out a continuous struggle for women's freedom from stereotypes and expectations with their actions.

Facial Beauty Prediction Based on Deep Learning: A Review

This review delves into Facial Beauty Prediction (FBP) using deep learning, specifically focusing on convolutional neural networks (CNNs). It synthesizes recent advancements in the field, examining diverse methodologies and key datasets like SCUT-FBP and SCUT-FBP5500. The review identifies trends in FBP research, including the evolution of deep learning models and the challenges of dataset biases and cultural specificity. The paper concludes by emphasizing the need for more inclusive and balanced datasets and suggests future research directions to enhance model fairness and address ethical implications.

Facial Beauty Standards Predictions Based on Machine Learning: A Comparative Analysis

This study uses a variety of machine learning and classification methods to anticipate the Facial Beauty Standards. The Accuracy of five different models—Random Forest, Logistic Regression, Support Vector Machine (SVM), KNN, and decision tree—were used to analyses each one. There were noticeable differences in the models' performances. In particular, the Logistic Regression and SVM methods demonstrate almost perfect accuracy, followed closely by random forest and KNN. This study gives insight into how well different models perform in comparison and emphasizes the benefits and drawbacks of each in terms of predicting face beauty standards.

A Novel Deep Neural Network for Facial Beauty Improvement

<p>This study delves into how to combine deep learning and fuzzy logic reasoning to evaluate facial aesthetics and provide targeted makeup recommendations. To further optimize the prediction results, we adopted the BLS method to correct the prediction residuals generated by ResNet-50. Specifically, the predicted appearance score can be expressed as score = p + δ, where p is the predicted result and δ represents the predicted residual of the system. After determining the beauty rating, we further studied four different makeup combinations (x1, x2, x3, x4). Moreover, we introduced fuzzy logic reasoning, defined fuzzy sets and fuzzy relationships, and established membership matrices for each makeup combination. The results of these fuzzy logical reasoning allow us to set a value range of m, n for each makeup method. Based on these reasoning results, we have come up with makeup recommendations for different facial aesthetics. Performance our system with the data collected from internet (accuracy of the calculation = 93.26%), from one volunteer (accuracy of the calculation = 98.14%) and from the both with different makeup skills (accuracy of the calculation = 95.63%) demonstrated that the visual sensing problem is feasible and will be a novel direction for the related engineering applications.</p> <p> </p>

Comparative Evaluation of Horizontal Lip Position With Ricketts Esthetic Plane in Preschool and School-Going Children of Bankura, West Bengal: A Craniofacial Anthropometric Study.

Introduction The horizontal lip position and esthetic plane are two important parameters to define facial beauty, and these factors are always given importance in children undergoing fixed orthodontic therapy. The purpose of this study was to evaluate horizontal lip position in primary and mixed dentition children with class I occlusion and to analyze its association with gender among preschool and schoolchildren of villages in the Bankura district in West Bengal. Materials and methods Researchers screened 437 children for the study and selected those who met the inclusion criteria. A total of 407 children were segregated: 201 children aged three to five years with the flush terminal plane and mesial step in primary teeth and 206 children aged seven to eleven years with class I occlusion in mixed dentition were selected from schools in villages in Bankura district, West Bengal, India. The subjects were instructed to hold the head in the natural head position by looking straight, and points were marked on the nose and chin tip, respectively. A metallic ruler was placed from nose to chin, representing Rickett's esthetic line. The horizontal lip distance to the esthetic plane of both upper and lower lips was measured as a linear distance from the most anterior part of the lip to the metallic ruler. The data were recorded, compared with gender, and statistically analyzed using the Chi-square test using the Statistical Package of Social Sciences software (SPSS version 19.0, 2015, IBM Corp., Armonk, NY). Results The most prevalent horizontal lip distance for both upper and lower lips to the esthetic line for primary and mixed dentition in the Bankura region of West Bengal was category I, where the lip is seen beyond the E plane, followed by category II, where lips are at a horizontal distance in the range of 0-1.5 mm from the esthetic line. A significant correlation of lip position with Rickets aesthetic plane was also illustrious with gender in primary dentition, but a non-significant association with gender was noted in mixed dentition. Conclusion Children with class I occlusion from the Bankura district of West Bengal showed a higher percentage of lip position beyond the esthetic plane in both primary and mixed dentition, which is not in line with the inference of Ricketts's studies on the Caucasian population. There is a definite association between gender and horizontal lip distance in Ricketts esthetic plane. Protrusive upper lips were seen more in males, and retrusive lips were seen more in females. Clinical significance The horizontal lip position with reference to Ricketts esthetic plane has been documented in the literature for adults and teens undergoing fixed orthodontic treatment. However, there is no study done to define these measurements in preschool and school-going children, which can assist in determining future esthetic profiles and in preparing a protocol for early age interceptive orthodontics along with aesthetic rehabilitation of the anterior area of the mouth.

Dynamic Attentive Convolution for Facial Beauty Prediction

Dynamic Attentive Convolution for Facial Beauty Prediction

The “Beauty Premium” Phenomenon: Appearance Discrimination in the P2p Market

The online P2P accommodation market, including Airbnb, encourages accommodation hosts to upload profile photos. However, the inclusion of a profile photo may carry consequences such as appearance discrimination. Using secondary Airbnb data from Beijing, China, this study investigates the presence of the “beauty premium” in the relatively low-priced accommodation market and examines the extent to which consumers discriminate based on hosts’ facial appearance from a supply perspective. Three experiments were conducted to respectively examine the impacts of hosts’ facial beauty on customers’ willingness-to-pay, boundary conditions, and underlying mechanisms. The findings emphasize the importance of hosts’ visual self-disclosure in reducing appearance-based discrimination. By providing practical implications for P2P platform operators and accommodation hosts, this research contributes to a better understanding of appearance-based biases in the online accommodation market and reveals strategies for mitigating negative effects.

Facial Beauty Prediction Based on Vision Transformer

Facial beauty analysis is a crucial subject in human culture among researchers across various applications. Recent studies have utilized multidisciplinary approaches to examine the relationship between facial traits, age, emotions, and other factors. Facial beauty prediction is a significant visual recognition challenge that evaluates facial attractiveness for human perception. This task demands considerable effort due to the novelty of the field and the limited resources available, including a small database for facial beauty prediction. In this context, a deep learning method has recently shown remarkable capabilities in predicting facial beauty. Additionally, vision Transformers have recently been introduced as novel deep learning approaches and have shown strong performance in various applications. The key issue is that the vision transformer performs significantly worse than ResNet when trained on a small ImageNet database. In this paper, we propose to address the challenges of predicting facial beauty by utilizing vision transformers instead of relying on feature extraction based on Convolutional Neural Networks, which are commonly used in traditional methods. Moreover, we define and optimize a set of hyperparameters according to the SCUT-FBP5500 benchmark dataset. The model achieves a Pearson coefficient of 0.9534. Experimental results indicated that using this proposed network leads to better predicting facial beauty closer to human evaluation than conventional technology that provides facial beauty assessment.

A novel framework for quantitative rhinoplasty evaluation by ResNet convolutional neural network

Rhinoplasty is a popular surgical operation, so proposing trustworthy assessment methods is crucial. Previous studies often utilized traditional or non-automatic methods for rhinoplasty evaluation, overlooked the aesthetic harmony of the nose with other facial features, and provided limited descriptions of facial beauty without detailed explanations. To address these limitations, we have developed a deep learning-based system for quantitative and qualitative facial beauty assessment and rhinoplasty results based on the random preoperative and postoperative color photographs of 376 patients, differentiating male and female faces. The quantitative evaluation includes automatically extracting 3D facial key points from frontal and lateral views, developing a novel mathematical 3D facial model, applying seven criteria from rhinoplasty literature, and assigning related scores. The qualitative evaluation comprises the design of a questionnaire, the extraction of facial features using a unique CNN-based algorithm, and the assignment of scores based on the questionnaire's results. Our method calculates the success percentage of rhinoplasty and provides precise and comprehensive quantitative and qualitative beauty scores. The accuracy of the proposed facial feature extraction network is 71 %, which is considered acceptable according to the complexity of defining beauty and the novelty of this work. All procedures and outcomes are verified by an ENT (Ear, Nose, and Throat) specialist. In particular, based on the presented extensive tables and histograms, some patients have lower postoperative scores than preoperative ones in some instances, which caused negative success scores. For this reason, individuals' appearance may occasionally worsen following rhinoplasty instead of improving. Therefore, preoperative assessments of facial features are crucial, and our proposed system facilitates this process. Our research also impacts individual self-assessment and surgeons' awareness significantly.

Anchor-Net: Distance-Based Self-Supervised Learning Model for Facial Beauty Prediction

Anchor-Net: Distance-Based Self-Supervised Learning Model for Facial Beauty Prediction

Analyzing VGG-19’s Bias in Facial Beauty Prediction: Preference for Feminine Features

Analyzing VGG-19’s Bias in Facial Beauty Prediction: Preference for Feminine Features

Facial Beauty Prediction Combined with Multi-Task Learning of Adaptive Sharing Policy and Attentional Feature Fusion

Facial beauty prediction (FBP) is a leading research subject in the field of artificial intelligence (AI), in which computers make facial beauty judgments and predictions similar to those of humans. At present, the methods are mainly based on deep neural networks. However, there still exist some problems such as insufficient label information and overfitting. Multi-task learning uses label information from multiple databases, which increases the utilization of label information and enhances the feature extraction ability of the network. Attentional feature fusion (AFF) combines semantic information and introduces an attention mechanism to reduce the risk of overfitting. In this study, the multi-task learning of an adaptive sharing policy combined with AFF is presented based on the adaptive sharing (AdaShare) network in FBP. First, an adaptive sharing policy is added to multi-task learning with ResNet18 as the backbone network. Second, the AFF is introduced at the short skip connections of the network. The proposed method improves the accuracy of FBP by solving the problems of insufficient label information and overfitting issues. The experimental results based on the large-scale Asia facial beauty database (LSAFBD) and SCUT-FBP5500 databases show that the proposed method outperforms the single-database single-task baseline and can be applied extensively in image classification and other fields.

A little note to God: A struggle against malignant cancer

Surat Kecil Untuk Tuhan (A note for God) is a book based on the true story of the struggle of an Indonesian teenage girl named Gitta Sesa Wanda Cantika or Keke against malignant cancer. Keke, who is only 13 years old, is a beautiful, smart girl and a former child singer who was suddenly diagnosed with a rare disease called rhabdomyosarcoma which is a kind of cancer that changes her face just that makes anyone have difficulty recognizing her. Cancer attacked her face and made her beautiful face look like a monster, the doctor said that she only had a few months to live. Hearing the verdict, Keke's father did not give up, he fought so that Keke could escape the death sentence. The father's struggle to save his daughter was so moving, Keke, who realized that her life would end, then wrote a small letter to God. God gave a gift in her life, and Keke was able to survive cancer for three years even though she finally gave up. This report was made with the aim of seeing how someone who is affected by a rare disease struggles and never gives up against cancer.

Facial Deformity and Self Concept, a Cross Sectional Study among Adolescents

Introduction - Today, beauty has become a marker for societal acceptance of a person. An ugly face can cause undue awkwardness& distress which may lead to various psychosocial problems. Facial deformity is the commonest cause leading to unattractive look. The adolescents get easily influenced and affected by facial deformity, as the importance of external beauty has been emphasized on their mind since childhood. To assess the Self Concept of the adolescents having Facial Deformity (Male and Female, Urban and Rural) Materials and Methods - The objectives were framed to see the influence of facial deformity, location (urban/rural) and gender on self-concept of adolescents. The sample size of the present study was 403 adolescents; the age group ranged from 12 to 18yrs. There were three independent variables which varied at two levels (with deformity-without deformity, urban-rural, male-female) and so 2 X 2 X 2 factorial design was used. The tool was administered and scoring was done in accordance with the scoring system given in the respective manual. Results - After statistical analysis, the results showed that the self-concept was low in adolescents with facial deformity, rural and female adolescents. Conclusion - Adolescents need to be counselled as facial beauty is not the only marker to build one’s self concept. Key Words - Adolescents, Facial deformity, Self-Concept

An Ensemble of Deep Convolutional Neural Networks Models for Facial Beauty Prediction

Facial beauty prediction is an emerging topic. The pursuit of facial beauty is the nature of human beings. As the demand for aesthetic surgery has increased significantly over the past few years, an understanding beauty is becoming increasingly important in medical settings. This work proposes a new ensemble based on the pre-trained convolutional neural network (CNN) models to identify scores for facial beauty prediction. These ensembles were originally built from the following previously trained models: DenseNet-201, Inception-v3, MobileNetV2, and EfficientNetB7. According to the SCUT-FBP5500 benchmark dataset, the proposed model obtains a Pearson coefficient of 0.9469. This reveals that the suggested EN-CNNs model can be successfully applied in a variety of face-to-face applications.