Makeup Style Research Articles

Assessment of radon levels in some cosmetics using CR-39 nuclear detector

People across the globe use cosmetics, thus studying radon in makeup is crucial for finding solutions to lessen its harmful impacts. The research is concerned with examining the amounts of radon and annual dose in cosmetics sold in Saudi Arabian marketplaces. The radioactive content of cosmetics was measured using the CR-39 detector. The results showed that the radon concentration values were within the internationally permissible limit, which ranges between 200 and 600 Bq/m3. The average value of radon concentration for all measured cosmetics was 237.94 Bq/m3. The results of the annual effective dose showed that 75% of the values were within the safe range. The average annual effective dose of radon was 4.11 mSv/year. Ads and Mineral products recorded values above the globally permitted values for all measured parameters. It is advised to stay away from high-value cosmetics. Using cosmetics with high radiation content can be harmful to the general public's health. It is advised to keep learning about makeup styles from around the globe.

Application of Makeup Image Optimization Recommendation System through the Analysis of BeautyGAN Based on Deep Learning

The purpose of this study was to identify the makeup preference of users and suggest a method to optimize the makeup style by using the preferred image for each age group through the analysis of BeautyGAN. Through this, you can propose a customized makeup style that suits you, and provide beneficial services to the makeup industry and consumers. In addition, by developing and validating new methods that effectively combine deep learning and vision systems, we aim to innovate makeup-related image conversion technology and contribute to academic and practical advances in this field. For this purpose, reference images suitable for each image were collected to implement image optimization for each age group, the input data reflected the researcher’s image, and the face was aligned and resized, after removing images with low resolution or poor lighting conditions. As a result of the performance evaluation of the BeautyGAN model, it was confirmed that the existing image was 51.26%, which is close to the BeautyGAN image of 38.89%. These results are judged to be able to provide customized makeup style suggestions or adjusted makeup effects that reflect the user’s preferences from an academic point of view, and from a practical point of view, it will be possible to improve the quality of customized beauty services by suggesting makeup styles that suit the characteristics of customers more accurately and quickly.

MuNeRF: Robust Makeup Transfer in Neural Radiance Fields.

There has been a high demand for facial makeup transfer tools in fashion e-commerce and virtual avatar generation. Most of the existing makeup transfer methods are based on the generative adversarial networks. Despite their success in makeup transfer for a single image, they struggle to maintain the consistency of makeup under different poses and expressions of the same person. In this paper, we propose a robust makeup transfer method which consistently transfers the makeup style of a reference image to facial images in any poses and expressions. Our method introduces the implicit 3D representation, neural radiance fields (NeRFs), to ensure the geometric and appearance consistency. It has two separate stages, including one basic NeRF module to reconstruct the geometry from the input facial image sequence, and a makeup module to learn how to transfer the reference makeup style consistently. We propose a novel hybrid makeup loss which is specially designed based on the makeup characteristics to supervise the training of the makeup module. The proposed loss significantly improves the visual quality and faithfulness of the makeup transfer effects. To better align the distribution between the transferred makeup and the reference makeup, a patch-based discriminator that works in the pose-independent UV texture space is proposed to provide more accurate control of the synthesized makeup. Extensive experiments and a user study demonstrate the superiority of our network for a variety of different makeup styles.

A robust transformer GAN for unpaired data makeup transfer

SummaryThe objective of makeup transfer is to apply the makeup style of, thereby creating the similar appearance as if it was professionally done. This technique has significant practical applications in fashion, beauty, and video special effects industries. However, there are several challenges faced by current makeup transfer models: (1) Low‐resolution images can only achieve partial makeup transfer in mainstream models. (2) Difficulty arises in obtaining paired data consisting of both makeup and non‐makeup images. (3) Spatial displacement occurs due to differences in subject and pose between reference and source images, affecting corresponding feature regions. (4) Mainstream models primarily focus on local feature characteristics while lacking global feature perception. To address these challenges, this paper proposes a nonpaired data makeup transfer model based on swin transformer generative adversarial networks. Additionally, an improved progressive generative adversarial network model (PSC‐GAN), incorporating semantic perception and channel attention mechanisms, is proposed to enhance the effectiveness of makeup transfer.

Personalized facial makeup transfer based on outline correspondence

AbstractMost existing makeup transfer techniques focus on light makeup styles, which limits the task of makeup transfer to color manipulation issues such as eye shadow and lip gloss. However, the makeup in real life is diverse and personalized, not only the most basic foundation, eye makeup, but also the painted patterns on the face, jewelry decoration and other personalized makeup. Inspired by the painting steps of drawing the outline first and then coloring, we propose a makeup transfer network for personalized makeup, which realizes face makeup transfer by learning outline correspondence. Specifically, we propose the outline feature extraction module and outline loss that can promote outline correspondence. Our network can not only transfer daily light makeup, but also complete transfer for complex facial painting patterns. Experiments show that our method can obtain visually more accurate makeup transfer results. Quantitative and qualitative experimental results show that the method proposed in this paper achieves superior results in extreme makeup transfer compared to the state‐of‐the‐art methods.

쿠사마 야요이(Kusama Yayoi) 작품의 표현 특징을 응용한 아트 메이크업 디자인 연구

This study attempted to analyze the expression characteristics of the artworks of Yayoi Kusama who have engaged in art activities actively through collaboration with other fields and suggest art makeup styles. In addition, it aimed to check the usability of such expression characteristics in art makeup and make a contribution to art makeup styling. For this, the characteristics were classified into the repetition of dots and infinity nets and color contrast, and their inner meaning was analyzed with self-obliteration and self-therapy. Based on these facts, a total of 6 art makeup styles were created, and the results found the followings: In Artworks I, II and III, visual interest was encouraged by generating rhythm with repetitive patterns. Such simple pattern was applied as a motive for unique design in art makeup. In Artworks IV, V and VI, obsessive dots and nets were still expressed, but it was able to confirm self-therapy because of the use of diverse colors. The expression characteristics of such artworks - ‘repetition of dots and infinity nets’ and ‘color contrast’ - were applied as diverse motives, and it was confirmed that they are suitable in art makeup. Furthermore, objects used in these artworks played a key role in enhancing their perfection through new expressions, escaping from conventional art makeup. The study results confirmed diverse expression methods and their usability in styling through a combination of artistic values and art makeup. As a result, there should be continued studies with artworks as motives according to recent trends. It is anticipated that such results would be available as a tool to develop unique and creative makeup styles.

Cover Art


 Artist Statement: I read through the abstracts and the creative pieces and what stood out to me were ideas of revolution, identity, progress, and change. I came up with the design by drawing purely from the aesthetic aspects of different theater traditions in Asia. For example, I looked at makeup styles from Kathakali and Kabuki, pattern designs from Wayang Kulit, and costuming from JingJu. This decision was made as a way to create something that people could identify as having Asian elements but not specific to any one country. The decision to include the fire is to communicate this idea of change/revolution and how that intersects with identity.
 
 
 Forest Wong, the artist, can be found on Instagram @forestwongart or at https://forest_wong.artstation.com/. 

Makeup transfer: A review

AbstractMakeup transfer (MT) aims to transfer the makeup style from a given reference makeup face image to a source image while preserving face identity and background information. In recent years, MT has attracted the attention of many scholars, and it has a wide range of application prospects and research value. Since then, many methods have been proposed to accomplish MT, most of which are based on Generative Adversarial Network methods. A taxonomy of existing algorithms in the field of MT is first proposed. Then, evaluation methods are proposed, existing methods are analysed, and existing datasets are introduced. This paper finally discusses the current problems in the field of MT and the trend of future research.

TSEV-GAN: Generative Adversarial Networks with Target-aware Style Encoding and Verification for facial makeup transfer

Generative Adversarial Networks (GANs) have brought great progress in image-to-image translation. The problem that we focus on is how to accurately extract and transfer the makeup style from a reference facial image to a target face. We propose a GAN-based generative model with Target-aware makeup Style Encoding and Verification, which is referred to as TSEV-GAN. This design is due to the following two insights: (a) When directly encoding the reference image, the encoder may focus on regions which are not necessarily important or desirable. To precisely capture the style, we encode the difference map between the reference and corresponding de-makeup images, and then inject the obtained style code into a generator. (b) A generic real-fake discriminator cannot ensure the correctness of the rendered makeup pattern. In view of this, we impose style representation learning on a conditional discriminator. By identifying style consistency between the reference and synthesized images, the generator is induced to precisely replicate the desirable makeup. We perform extensive experiments on the existing makeup benchmarks to verify the effectiveness of our improvement strategies in transferring a variety of makeup styles. Moreover, the proposed model is able to outperform other existing state-of-the-art makeup transfer methods in terms of makeup similarity and irrelevant content preservation.

The Comparison and Analysis of Make-up and Hair Style of International and Domestic Beauty Contest

This study is about beauty style focused on the grand prize winner of the Miss World Korea contest held independently in 2011 being separated from Miss Korea contest, the winners of the Miss Queen Korea contest called national representative beauty trio, and Mis Jin who is the main winner Miss Korea contest. It was conducted through comparative analysis of makeup and hairstyles in world beauty contests and domestic beauty contests. Comparing and analyzing different patterns through the characteristics of makeup and hairstyles in international beauty contests and domestic beauty contests is expected to be used as a source of new pattern research and a material of marketing in beauty industry in the future and there is a purpose that it contributes to the provocation for Korea to be a global beauty powerhouse. The makeup and hairstyles of beauties were analyzed using research papers books, Miss Queen Korea site, photo collection of Miss Korea site, Internet search, and interviews with contestants and other winners. As a result of the analysis, the world beauty pageant has a more apparent shading and smoky makeup to express sexy with boldness overly emphasizing the eye hole, and the hairstyle is also definitely given back comb to be exaggeratedly inflated, making the face look small, and the western image of a seductive style. The domestic beauty pageant broke away from the flashy and shady makeup of the past and changed to an elegant and calm natural makeup style, and the hair style was also changed from being expanded and inflated to calm and oriental image of intellectual and announcer. In previous studies, there was a close relationship between the trend beauty style and the beauty style of the beauty pageant, but in particular, in the case of the international beauty pageant, the trend and the beauty style of the beauty contest were expressed in a different form due to disimilar needs against the past, and there was no significant relationship.

SSAT: A Symmetric Semantic-Aware Transformer Network for Makeup Transfer and Removal

Makeup transfer is not only to extract the makeup style of the reference image, but also to render the makeup style to the semantic corresponding position of the target image. However, most existing methods focus on the former and ignore the latter, resulting in a failure to achieve desired results. To solve the above problems, we propose a unified Symmetric Semantic-Aware Transformer (SSAT) network, which incorporates semantic correspondence learning to realize makeup transfer and removal simultaneously. In SSAT, a novel Symmetric Semantic Corresponding Feature Transfer (SSCFT) module and a weakly supervised semantic loss are proposed to model and facilitate the establishment of accurate semantic correspondence. In the generation process, the extracted makeup features are spatially distorted by SSCFT to achieve semantic alignment with the target image, then the distorted makeup features are combined with unmodified makeup irrelevant features to produce the final result. Experiments show that our method obtains more visually accurate makeup transfer results, and user study in comparison with other state-of-the-art makeup transfer methods reflects the superiority of our method. Besides, we verify the robustness of the proposed method in the difference of expression and pose, object occlusion scenes, and extend it to video makeup transfer.

GAYA HIDUP IMITASI PENGGEMAR DRAMA KOREA DI PROGRAM STUDI PENDIDIKAN SOSIOLOGI FKIP UNIVERSITAS TANJUNGPURA PONTIANAK

The purpose of this study is to describes the cognitive processes (attention, retention,production of behavior, and motivation) of fans who imitated Korean drama in theSociology Education Study Program, FKIP, Tanjungpura University Pontianak, usingAlbert Bandura's social learning theory. Research approach used qualitative approachdescriptive form. Data collection techniques used are observation, interview, anddocumentation. Data collection tools using observation guides, interview guides,documentation tools. Data analysis techniques used are data reduction, datapresentation, and conclusion. The research analysis was presented descriptivelythrough 3 informants as students of Korean drama fans. The results of this study showedthat the process of fan attention that imitates Korean dramas is the appearance of goodlooking artists, it attracts the attention of informants, from fashionable dress styles tonatural makeup styles. the retention process is watching the drama and updating theartist's social media posts every day in their spare time. Behavioral production processis to imitate fashion in the form of outer knitting, mini dress and natural make up style,by buying a variety of supporting local products that are cheaper in the Indonesianonline marketplace. The motivation of fans is that they want to look stylish and cool likeKorean drama artists. By imitating this, making brands confident because the style ofappearance is in accordance with their tastes.Keywords: Imitation Lifestyle, Korean Drama Fans, Social Learning.

Lipstick color suggested using hybrid hidden Markov model

In today’s lifestyle, facial makeup plays an important role in enhancing visual attractiveness a nd boosting one’s self-esteem. Makeup is known as the second skin of the women, and that too lipstick is a soul of the makeup. According to a survey, an average urban woman spends almost one year and three months of her life just wearing makeup. This much of the time is due to decision-making and choosing the best makeup style that would fit a particular age, skin complexion, profession, and occasion, requiring high imagination and full art. Many makeup recommendations systems exist, but no solitary system has existed to recommend a lipstick color properly to solve this problem computationally. Therefore, as a tiny step towards helping women save their time in decision-making, the proposed model puts forward an idea of a hybrid recommendation model for lipsticks alone. This proposed methodology uses a hidden Markov model to unleash the possible color of the lipsticks based on the given attributes. The collaborative filtering system catalyzes the process to recommend the best lipstick colors using a hybrid recommendation model.

CUMTGAN: An instance-level controllable U-Net GAN for facial makeup transfer

Instance-level makeup transfer refers to transferring a specific makeup style to a particular face without cosmetic, while keeping its facial structure. To solve the challenges such as face structure loss and uncontrollable shade of makeup transfer in the existing makeup transfer methods, in this paper, we propose an instance-level face makeup transfer algorithm with generative adversarial network (CUMTGAN, Controllable U-Net Makeup Transfer GAN). The network uses the results of face semantic segmentation to put on or remove makeup on specific areas such as eyes, lips, while constraining the parts such as image background. It uses the characteristics of the skip connections of the U-Net structure to achieve hierarchical feature fusion. A Superimposed Module is also proposed to provide a controllable shade of makeup for the generated images, making the control of the makeup more arbitrary. Especially, we propose a new quantitative evaluation method to evaluate facial structure and makeup style respectively. To our best knowledge, it is the first time to evaluate generated samples from two aspects. The experimental results on the MT dataset show that the proposed method can generate natural and visual makeup images and makeup removal images, which is superior to the baseline methods in facial structure and makeup style.

Facial makeup transfer with GAN for different aging faces

Facial aging is widely used in criminal tracking and the search for lost children. If the aging face is made up, it will greatly affect the discrimination of the tracking system. Therefore, the research on the makeup of different aging faces is extremely important. Existing studies have achieved a good transition from the non-makeup domain to the makeup domain in facial makeup transfer. But few studies involve the transfer of facial makeup at different ages. In addition, existing datasets rarely contain both age and makeup attributes, which make the transfer of facial makeup for different ages full of challenges. To solve the above problems, we propose a learning framework, called AM-Net, which can realize facial makeup transfer for different ages while protecting identity information. AM-Net is composed of two sub-network modules: Aging-Net and Makeup-Net. AM-Net first learns the aging mechanism of faces through Aging-Net, and then, it feeds the learned aging mode to Makeup-Net. After that, AM-Net trains Makeup-Net to realize the mapping relationship between the non-makeup domain to the makeup domain and transfer the makeup style to the face of the non-makeup. Throughout the network, multiple losses are applied to ensure AM-Net preserve information about the identity, background, etc. Extensive experiments are conducted on different datasets with different state-of-the-art methods, which prove the effectiveness of AM-Net.

RAMT-GAN: Realistic and accurate makeup transfer with generative adversarial network

Automatic makeup transfer aims to precisely transfer the makeup style from a given reference makeup face image to a source image while preserving face identity and background information. Current works have achieved promising results in makeup transfer by using deep learning techniques. However, existing methods are focused on only one or two requirements, thus cannot simultaneously achieve face identity preservation, background retention, and accurate makeup transfer. Besides, it is also difficult to acquire a pair of well-aligned face images with different makeup styles. Aimed at these problems, we propose RAMT-GAN (Realistic and Accurate Makeup Transfer Generative Adversarial Network), a GAN-based image transformation framework for achieving realistic and accurate makeup style transfer. Specifically, we utilize a dual input/output network that builds on the BeautyGAN architecture to achieve cross-domain image transformation. Then, identity preservation loss and background invariant loss are introduced in RAMT-GAN to help synthesize realistic and accurate face makeup images. Extensive experiments demonstrate that the proposed makeup transfer model can synthesize makeup faces with accurate reference style as well as maintaining the identity information and the background information.

Large-pose facial makeup transfer based on generative adversarial network combined face alignment and face parsing.

Facial makeup transfer is a special form of image style transfer. For the reference makeup image with large-pose, improving the quality of the image generated after makeup transfer is still a challenging problem worthy of discussion. In this paper, a large-pose makeup transfer algorithm based on generative adversarial network (GAN) is proposed. First, a face alignment module (FAM) is introduced to locate the key points, such as the eyes, mouth and skin. Secondly, a face parsing module (FPM) and face parsing losses are designed to analyze the source image and extract the face features. Then, the makeup style code is extracted from the reference image and the makeup transfer is completed through integrating facial features and makeup style code. Finally, a large-pose makeup transfer (LPMT) dataset is collected and constructed. Experiments are carried out on the traditional makeup transfer (MT) dataset and the new LPMT dataset. The results show that the image quality generated by the proposed method is better than that of the latest method for large-pose makeup transfer.

BeautyGAN+: mixed-supervised makeup transfer learning algorithm based on new PMT dataset

As one of the important ways to change the appearance of face image, makeup transfer has received more and more attention in recent years. Makeup transfer networks can translate the makeup style of a reference image to any other non-makeup one while preserving face identity, helping people find the most suitable makeup for them and get the beautified image. BeautyGAN, a makeup transfer network, has successfully demonstrated the results of unsupervised makeup transfer. However, unsupervised learning strategies lack supervised labels that can provide makeup details, leading to problems such as background discoloring, artifacts on edges and illumination disturbance in the experimental results. Constraint of BeautyGAN with makeup and non-makeup pairwise images can solve this problem, but academia lacks such fine-labeled paired dataset. To solve this problem, we propose BeautyGAN+, which adds supervised loss to BeautyGAN to train, and build up a new makeup dataset, the PMT dataset, that consists of makeup and non-makeup pairwise images. Finally, we find that combining unsupervised (migration) and supervised (details) loss can get better results through experiments. The experimental results and user study (14 participants) indicate that BeautyGAN+ significantly improves the results of makeup transfer, solving the problem of lack of paired dataset and various deviations in the transfer results.

A Technical Approach for Promotion of Yakshagana - A Literature Survey

Background/Purpose: Yakshagana is a theatre style that combines varieties of make-up styles and dressing styles depending on the type of character or role that is to be played on the stage. This article briefly discusses certain areas of Yakshagana where technology can be used for object detection and make-up recognition to classify and identify the characters. Objective: The main objective of this literature survey is to summarize the techniques used to identify different objects in a Yakahagana image & to identify the different makeup styles to recognize the type of characters. This paper also recommends the technical methods that can be used to promote Yakshagana. Methodology: Information is collected from various scholarly articles, books, and web pages. Findings/Results: Based on the analysis of the papers reviewed, it is observed that, no much work is carried out in the field of object detection & make-up detection in Yakshagana. This survey paper recommends techniques and the methodology that can be applied to the Yakshanaga field to identify and classify the objects and detect the patterns in makeup in Yakshagana roles. Limitations: The study is limited to crown & jewelry detection and pattern recognition in facial makeup in Yakshagana Originality: This paper focuses on the summary of techniques used to identify the objects, facial recognition, and pattern in make-up detection. Paper Type: A Literature survey paper on techniques and methods used to meet the required objectives.

A Study on the Digital Makeup Design Using Retro Characteristics

As a new post-COVID-19 trend, non-contact digital technologies have been applied to various fields. This study attempted to create digital makeup styles based on retro design characteristics according to the new trends in the digital culture, using Adobe Photoshop CC2020, a tablet PC, and a stylus pen after scanning model photos. The retro design characteristics were categorized into retro, romantic, ethnic, and Kitsch aspects, and the results found the following: In the retro aspect, retro makeup styles from the 1990s were reproduced by recalling memories. In the romantic style, elegant and feminine romanticism was expressed. From an ethnic perspective, ethnic makeup styles were created with liberal and passionate colors mixing indigenous and oriental images. In terms of Kitsch, makeup styles were created entertainingly through the free expression of inner emotions, using anti-aesthetic and playful painting techniques. The above results confirmed the characteristics and importance of retro design. This study was able to maximize the effects of works by taking advantage of retro styles characteristics. Furthermore, it is possible to simulate identical makeup using data on an actual model. Considering the current trends, digital content is expected to spread around the globe. Therefore, the suggestion of creative design by makeup artists would further broaden makeup-related fields. It is anticipated that this study would be a starting point for expanding creative and unique designs and the styling sectors.