Data-Driven Research on the Matching Degree of Eyes, Eyebrows and Face Shapes

Trends in eyebrow makeup after COVID-19 and long-COVID era

Background and Objectives

The coronavirus disease-19 (COVID-19) pandemic, which began in 2019, is threatening millions of people around the world. Coronavirus, a severe acute respiratory syndrome, made it mandatory to wear masks, it was carried out through public awareness and review of changes in cosmetics.

Methods

This literature review paper was written by referring to keywords such as "Eyebrow," "Permanent Make-up," "Microblading," "Make-up," and "COVID-19." The study selected a total of 485 references using representative journal search sites such as PubMed, Google Scholar, ResearchGate, RISS, DBPia, and CrossRef, of which a total of 43 papers were selected at the final stage from 2000 to 2022 using PRISMA flow diagram.

Results

With the wearing of a mask due to COVID-19, we are paying attention to the change in the makeup trend caused by the preference for easy eye makeup.

Conclusions

This narrative review understands that eyebrow makeup has a significant impact on human images due to changes in makeup methods after the COVID-19 pandemic. It is expected to be used as important data for the rapidly growing semi-permanent makeup market.

Large irides enhance the facial attractiveness of Japanese and Chinese women

Since large eyes are often perceived to enhance facial attractiveness, many individuals are motivated to change their eyes' appearance. Colored contact lenses are often used by young women to darken the limbal rings of their irides, to increase their facial attractiveness. Among Westerners, wearing contact lenses with limbal rings enhances facial attractiveness; a similar effect might exist for East Asians whose irides are darker; although, the mechanism underlying these preferences in Westerners and East Asians might differ. In this study, we investigated whether larger irides increased the attractiveness of Japanese and Chinese women, and whether this effect was accompanied by changes in perceived friendliness or youthfulness. We manipulated eye size by enlarging only the iris or the whole eye, and asked participants to rate face stimuli in terms of facial attractiveness, friendliness, and youthfulness. We found that larger irides enhanced not only attractiveness but also perceived youthfulness and friendliness, and that there was a significant correlation between attractiveness and friendliness. Further, iris-enlarged faces were perceived as more attractive even when the iris was originally dark, as enlarged dark irides were confused with dilated pupils, which are often perceived as a sign of attraction.

Putative ratios of facial attractiveness in a deep neural network

Empirical evidence has shown that there is an ideal arrangement of facial features (ideal ratios) that can optimize the attractiveness of a person's face. These putative ratios define facial attractiveness in terms of spatial relations and provide important rules for measuring the attractiveness of a face. In this paper, we show that a deep neural network (DNN) model can learn putative ratios from face images based only on categorical annotation when no annotated facial features for attractiveness are explicitly given. To this end, we conducted three experiments. In Experiment 1, we trained a DNN model to recognize the attractiveness (female/male × high/low attractiveness) of face in the images using four category-specific neurons (CSNs). In Experiment 2, face-like images were generated by reversing the DNN model (e.g., deconvolution). These images depict the intuitive attributes encoded in CSNs of the four categories of facial attractiveness and reveal certain consistencies with reported evidence on the putative ratios. In Experiment 3, simulated psychophysical experiments on face images with varying putative ratios reveal changes in the activity of the CSNs that are remarkably similar to those of human judgements reported in a previous study. These results show that the trained DNN model can learn putative ratios as key features for the representation of facial attractiveness. This finding advances our understanding of facial attractiveness via DNN-based perspective approaches.

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.