Assessment Of Beauty Research Articles

GOLDEN RATIO INTERPRETATION ON 150 FLOWERS: THE BASIC INFORMATION TO DEVELOP ECOTOURISM IN BOGOR, INDONESIA

The Aesthetic value is one of the factors in order to attract tourists to visit. Flowers are one form of tourist attraction that symbolizes beauty, flowers can be used as an object of interpretation for the development of ecotourism, especially in interpreting the meaning of the aesthetic value. Assessment of beauty can be measured by a number that is believed to have beauty, namely by using the golden ratio. Research has shown that the golden ratio actually exists in flowers. The knowledge of the golden ratio of flowers, in addition to increasing tourist attraction, can also be used as an interpretation for tourists because the aesthetic value is very important to the human relationship with nature, and the motivation to protect it. Golden ratio analysis was performed on 150 flowers. A flower is said to have a golden ratio if the ratio results in a comparison of flower morphology producing a number of 1.618 or close to that number. The discovery of the golden ratio in all flowers shows that flowers are creations that have aesthetic value so visitors are expected to be able to interpret the importance of the value of beauty in living things for their tour experience

Beyond functionality: topographical complexity as a driver of aesthetic value in urban green infrastructure

Cities face growing challenges from stormwater: flooding, pollution, and habitat loss. Green infrastructure offers solutions, yet the aesthetic dimension is often overlooked. This study introduces the Buras Beauty Scale for objectively assessing the beauty of wetland configurations and emphasises its integration into green infrastructure projects. Our methodology involved surveying diverse groups, including non-architecture students and non-traditional architects, who perceived higher beauty in wetland configurations compared to traditional architects and architecture students (mean beauty scores: 7.8 and 7.5 vs. 6.2 and 6.4, respectively). Statistical analysis revealed a strong correlation (r = 0.731, p < 0.05) between beauty assessments and Shannon-Weaver entropy, highlighting the importance of topographic diversity. We also observed that higher beauty scores correlated with greater consensus among respondents, as evidenced by a decrease in the coefficient of variation from 0.52 to 0.34. Multicriteria analyses, integrating beauty with functionality, identified optimal wetland configurations for urban landscapes.

Prediksi Keindahan Wajah dengan Menggunakan Attribute Aware Convolutional Neural Network (AACNN)

The perception of beauty whether applied to a face or any similar subject, differs among people, with assorted values doled out to magnificence levels or positions. Facial beauty assessment has gained significant interest in various domains, including cosmetics, image processing, and artificial intelligence. This review digs into facial beauty prediction utilizing the Attribute Aware Convolutional Neural Network (AACNN) according to the SCUT-FBP5500 benchmark dataset. The study in this paper closes by accentuating the requirement for more comprehensive and adjusted datasets and proposes future exploration bearings to improve model decency and address moral ramifications.

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.

An unbiased artificial referee in beauty contests based on pattern recognition and AI

Beauty contests have long been popular, but concerns about fairness and bias in judgment have emerged. To address this, integrating artificial intelligence (AI) and pattern recognition (PR) as an unbiased referee shows promise. This paper aims to assess the significance of different facial features, including eyes, nose, lips, chin, eyebrows, and jaws, as well as the role of angles and geometric facial measurements, such as distances between facial landmarks and ratios, in the context of beauty assessment. This study also employs two techniques, namely Principal Component Analysis (PCA) and stacked regression, to predict the attractiveness of faces. The experimental data set used for evaluation is the SCUT-FBP benchmark database. The obtained results, indicated by Mean Absolute Errors (MAE) and Pearson's Correlation Coefficient (PCC), demonstrate the high accuracy of our attractiveness prediction model. This research contributes to the advancement of automatic facial beauty analysis and its practical implications. Furthermore, our results surpass those published recently, further validating the effectiveness of our approach.

Contribution of Affect and Cognition in Shaping Aesthetic Responses

The human experience of art is unique, being associated with exceptional and transcendent states of consciousness. These experiences are formed based on the interaction between the work of art itself, the observer, and the context. Using the multilevel modelling with crossed random effects analysis, we explored the connection between the affective and cognitive appraisals of a visual work of art and the aesthetic judgment. Two experiments were performed. In the first, lay persons appraised figural paintings with pleasant and unpleasant content. In the second experiment, abstract and realistic paintings were appraised. The results indicate a strong connection between affective appraisal and the aesthetic judgment, especially with the assessment of beauty. Cognitive appraisals are less closely connected to aesthetic appraisals. These results suggest that naive observers use everyday, practical perception when evaluating works of art, and that paintings which evoke "sweet", pleasant, hedonic emotions will be assessed as more aesthetically pleasing. The deviation of a painting from traditional art canons further strengthens the reliance on everyday perception in lay persons. The implications of these results are discussed.

Implementation of an Attention Mechanism Model for Facial Beauty Assessment Using Transfer Learning

An important consideration in medical plastic surgery is the evaluation of the patient's facial symmetry. However, because facial attractiveness is a slightly individualized cognitive experience, it is difficult to determine face attractiveness manually. This study aimed to train a model for assessing facial attractiveness using transfer learning while also using the fine-grained image model to separate similar images by first learning features. In this case, the system can make assessments based on the input of facial photos. Thus, doctors can quickly and objectively treat patients' scoring and save time for scoring. The transfer learning was combined with CNN, Xception, and attention mechanism models for training, using the SCUT-FBP5500 dataset for pre-training and freezing the weights as the transfer learning model. Then, we trained the Chang Gung Memorial Hospital Taiwan dataset to train the model based on transfer learning. The evaluation uses the mean absolute error percentage (MAPE) value. The root mean square error (RMSE) value is used as the basis for experimental adjustment and the quantitative standard for the model's predictive. The best model can obtain 0.50 in RMSE and 18.5% average error in MAPE. A web page was developed to infer the deep learning model to visualize the predictive model.

The beautiful discourse of Sundanese women in Bandung City

Discourse is a system of thinking, ideas, or thoughts describing a culture or speech characteristic in a certain period. This article focuses on the discourse of beautiful Sundanese women in Bandung. This research is studied using Michel Foucault’s theory of discourse, with the archaeological method of knowledge to determine the discontinuity between one knowledge and another. Data collection techniques with in-depth interviews and documentation study. The results of this study indicate that the discussion of beautiful Sundanese women in the city of Bandung is first, beauty is not only judged by physical appearance, but the assessment of beauty is based on behavior and thoughts; second, women are considered beautiful when they are grateful, confident, find their unique beauty, and love themselves; and third, there is no the same physical standard, however, Sundanese women in Bandung expect healthy and clean facial skin. This study concludes that the discourse of beauty in Sundanese women was formed as a form of expression to maintain and love themselves and not about specific predetermined standards of beauty.

Utilization of Machine Learning for the Objective Assessment of Rhinoplasty Outcomes

Machine Learning started to provide solutions to various challenges in many fields, including medicine. The objective assessment of rhinoplasty results has been a challenge since the assessment of beauty is subjective in nature. This study explores if Machine Learning can be used to accomplish the complex task of objective evaluating the outcome evaluation and automated scoring for rhinoplasty. We introduce a methodology to map the aesthetics of visual appearance to the quantified measurements of pre-surgery, planned outcome, and post-surgery using machine learning. To develop the methodology, we generated synthetic 3D models utilizing artificial intelligence tools and applied various nasal deformities to simulate the pre-surgery, planned outcome, and post-surgery scans of rhinoplasty patients. The simulated outcomes were scored by reviewing the 3D visuals and corresponding measurements to prepare the training data for machine learning models. AutoGluon AutoML framework is used to generate the best-performing machine learning model. We successfully developed machine learning models with accuracy between 82% to 88% depending on the scoring method. We also identified the measurements that are highly influential in determining the scores. This is the first study that correlates the visual appearance and quantitative facial measurements of simulated rhinoplasty outcomes. The results suggest that an AI-based objective rhinoplasty outcome scoring tool is possible when machine learning algorithms are trained using consensus scores along with patients’ pre-surgery, planned, and post-surgery measurements. This study introduces a methodology regarding how to map the aesthetics of visual appearance to the quantified measurements of pre-surgery, planned outcome, and post-surgery using machine learning.

The Nexus of the Objective Assessment of Beauty and the Subjective Aesthetic Cognition

Exploring the aesthetic appraisal of surfaces through texture and the balance of the shapes and colors is a significant issue in reaching aesthetic evaluation, and setting the evaluators on common ground and a solid reference point in judging aesthetic values. This paper aims to find a guideline for designers to understand the characteristics of beautiful texture of surfaces for human well-being in inner spaces. Moreover, the study approached case study methodology to evaluate the aesthetic value of the texture in inner spaces objectively and subjectively. The objective method was conducted by using Birkhoff’s aesthetic measure, and subjective method by asking for the personal opinions of the professionals. Hence, three works of interior architectural students have been selected as case studies for this purpose. The comparison of the results from both objective and subjective methods has been carried out and the results have been extrapolated. The results showed positive credibility for Birkhoff's aesthetic measure tool in comparing different case studies aesthetically with the aesthetic cognition of the professionals. The study also, concluded that the aesthetic value of surface texture is prompted by enhancing order and decreasing complexity.

Conceptualization of Facial Beauty Among Female Students in a Southwestern Nigerian University Using the Golden Ratio Model

Introduction: The golden ratio is a mathematical formula proposed over the years to assess facial beauty objectively. The correlation between the golden ratio and facial attractiveness has been researched in the Caucasian and Asian populations, with little literature on the African population. This study aimed to establish a baseline study on the relationship between subjective assessment of facial beauty and the golden ratio among female students of the University of Medical Sciences, Ondo, Nigeria. Materials and Methods: One hundred female students aged 1622 years were mobilized for the study. The facial landmarks that Gary Meisner proposed and the PhiMatrix software to analyze golden facial ratios were employed. A relationship between the subjective perception of beauty and the golden ratio was established by having observers rate the photographs; based on this; the sample was grouped into esthetically pleasant, unpleasant, and acceptable. The golden facial ratios were then measured in these classified faces. Results: The result revealed that 68 females from the study sample conformed to the golden ratio. The highest percentage from the esthetically pleasant groups was significant at P &lt; 0.05. When these ratios were isolated and tested, only four of the ratios were substantial, at P &lt; 0.05. Conclusion: From this result, it is possible to infer a relationship between the golden ratio and facial attractiveness. However, it may not be used as an objective measure of facial beauty because many faces classified as acceptable and unattractive also exhibit golden proportions.

Strategi Perusahaan Kosmetik Korea Untuk Memperkuat Marketing Di Indonesia

Assessment of beauty in other countries is very different, this shows that beauty is something that is very relative and cannot be measured. Several factors cause the construction of beauty in each area to be different, one of which is the cultural factor. It is undeniable, in the current era of globalization, industrial development is growing rapidly in countries around the world. Beauty is very important for Korean society, especially women. This is the basis why cosmetics are the primary needs of society and the demand for cosmetics in the market continues to increase from year to year. In the case of Korea, the number of Muslim tourists is increasing year by year, and their Korean cosmetic purchases are also increasing rapidly. In this study, the author uses a qualitative method by means of structured interviews regarding the use and interest in cosmetic products originating from Korea by explaining several reference questions, including: This study tries to suggest a more successful entry strategy for Korean cosmetic companies in the Indonesian cosmetic market. However, although a successful entry strategy in this study can explain the Indonesian cosmetic market, it is difficult to explain it as a successful entry strategy into other Southeast Asian countries or Islamic cultural markets.&#x0D; Keywords: Cosmetics; Marketing; Strategy

Personalized facial beauty assessment: a meta-learning approach

Personalized facial beauty assessment: a meta-learning approach

A Bayesian evaluation framework for subjectively annotated visual recognition tasks

An interesting development in automatic visual recognition has been the emergence of tasks where it is not possible to assign objective labels to images, yet still feasible to collect annotations that reflect human judgements about them. Machine learning-based predictors for these tasks rely on supervised training that models the behavior of the annotators, i.e., what would the average person’s judgement be for an image? A key open question for this type of work, especially for applications where inconsistency with human behavior can lead to ethical lapses, is how to evaluate the epistemic uncertainty of trained predictors, i.e., the uncertainty that comes from the predictor’s model. We propose a Bayesian framework for evaluating black box predictors in this regime, agnostic to the predictor’s internal structure. The framework specifies how to estimate the epistemic uncertainty that comes from the predictor with respect to human labels by approximating a conditional distribution and producing a credible interval for the predictions and their measures of performance. The framework is successfully applied to four image classification tasks that use subjective human judgements: facial beauty assessment, social attribute assignment, apparent age estimation, and ambiguous scene labeling.

Концепты «Небо» и «Земля» в устойчивых словосочетаниях и выражениях сравнений красоты женщин разных культур

Research relevance in this article is the study of concepts Heaven and Earth, through phraseological units belonging to carriers of different-structured languages, which reflect female beauty, determine their universal and national-unique characteristics. Research purpose is to study and analyze the features of stable expressions about the beauty of women associated with the concept Heaven and Earth in different cultures. Research methods: we tried to understand and identify how the beauty of women is reflected in the stable expressions of different peoples, where the culture and assessment of female beauty is preserved in comparison with heavenly bodies and with all earthly beauty on our planet. Research results can be used: in the practice of teaching the course of comparative typology, lexicology of the Russian, Kyrgyz and English languages, as well as in teaching Russian, Kyrgyz and English on a linguacultural basis, both in foreign and national audiences. Conclusions: the semantic spaces of different languages made it possible to compare them with the subsequent allocation of universal universals, the national specifics of the concept sphere, since the concept sphere and the semantic space of a language have a common nature, since they are mental entities.

Facial Beauty Prediction From Facial Parts Using Multi-Task and Multi-Stream Convolutional Neural Networks

Automatic analysis of facial beauty has become an emerging computer vision problem in recent years. Facial beauty prediction (FBP) aims at developing a human-like model that automatically makes facial attractiveness predictions. In this study, we present and evaluate a face attractiveness prediction approach using facial parts as well as a multi-task learning scheme. First, a deep convolutional neural network (CNN) pre-trained on massive face datasets is utilized for face attractiveness prediction, which is capable of automatic learning of high-level face representations. Next, we extend our deep model to other facial attribute recognition tasks. Hence, a multi-task learning scheme is leveraged by our deep model to learn optimal shared features for three correlated tasks (i.e. facial beauty assessment, gender recognition as well as ethnicity identification). To further enhance the attractiveness computation accuracy, specific regions of face images (i.e. left eye, nose and mouth) as well as the whole face are fed into multi-stream CNNs (i.e. three two-stream networks). Each two-stream network adopts a facial part as well as the full face as input. Extensive experiments are conducted on the SCUT-FBP5500 benchmark dataset, where our approach indicates significant improvement in accuracy over the other state-of-the-art methods.

The Impact of Breast Symmetry on Eye Movement and Gaze Pattern: An Eye-Tracking Investigation.

The objective assessment of beauty remains challenging despite ongoing research efforts. A new method of objectively determining the aesthetic appeal of body features by analyzing gaze patterns and eye movements has recently been investigated. The objective of this study was to assess, by means of objective eye-tracking technology, differences in the gaze patterns of observers presented with standardized 3-dimensional images showing different degrees of breast asymmetry. A total of 83 Caucasian study participants with a mean age of 38.60 (19.8) years were presented with 5 images depicting varying degrees of breast asymmetry. In addition to the assessment of eye movements, participants were asked to rate the aesthetic appeal and the asymmetry of the breasts on a 5-point Likert scale. Overall, the data showed that participants' rating of the breasts' aesthetic appeal was inversely related to the level of asymmetry. The mean [standard deviation] time until fixation was shortest for the image depicting the greatest breast asymmetry (50 cc) at 0.77 [0.7] seconds (P < 0.001). In addition, the mammary region was also viewed longest in this image at 3.76 [0.5] seconds (P < 0.001). A volume difference of 35 cc between breasts deflected the observers' gaze significantly toward the larger of the asymmetric breasts (P < 0.001). Surgeons should focus on achieving symmetric breast volume (ie, differences <35 cc between breasts) to avoid noticeable asymmetry with regard to breast size.

Shaker Vision: Seeing Beauty in Early America. By Joseph Manca

Shaker Vision: Seeing Beauty in Early America. By Joseph Manca

Machine Learning-Based Facial Beauty Prediction and Analysis of Frontal Facial Images Using Facial Landmarks and Traditional Image Descriptors.

The beauty industry has seen rapid growth in multiple countries and due to its applications in entertainment, the analysis and assessment of facial attractiveness have received attention from scientists, physicians, and artists because of digital media, plastic surgery, and cosmetics. An analysis of techniques is used in the assessment of facial beauty that considers facial ratios and facial qualities as elements to predict facial beauty. Here, the facial landmarks are extracted to calculate facial ratios according to Golden Ratios and Symmetry Ratios, and an ablation study is performed to find the best performing feature set from extracted ratios. Subsequently, Gray Level Covariance Matrix (GLCM), Hu's Moments, and Color Histograms in the HSV space are extracted as texture, shape, and color features, respectively. Another ablation study is performed to find out which feature performs the best when concatenated with the facial landmarks. Experimental results show that the concatenation of primary facial characteristics with facial landmarks improved the prediction score of facial beauty. Four models are trained, K-Nearest Neighbors (KNN), Linear Regression (LR), Random Forest (RF), and Artificial Neural Network (ANN) on a dataset of 5500 frontal facial images, and amongst them, KNN performs the best for the concatenated features achieving a Pearson's Correlation Coefficient of 0.7836 and a Mean Squared Error of 0.0963. Our analysis also provides us with insights into how different machine learning models can understand the concept of facial beauty.