• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.22 no.3
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• pp.1-8
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• 2022

Recently, Google MedeiaPipe presents a novel approach for neural network-based hair segmentation from a single camera input specifically designed for real-time, mobile application. Though neural network related to hair segmentation is relatively small size, it produces a high-quality hair segmentation mask that is well suited for AR effects such as a realistic hair recoloring. However, it has undesirable segmentation effects according to hair styles or in case of containing noises and holes. In this study, the energy function of the test image is constructed according to the estimated prior distributions of hair location and hair color likelihood function. It is further optimized according to graph cuts algorithm and initial hair region is obtained. Finally, clustering algorithm and image post-processing techniques are applied to the initial hair region so that the final hair region can be segmented precisely. The proposed method is applied to MediaPipe hair segmentation pipeline.

• International Journal of Advanced Culture Technology
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• v.9 no.4
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• pp.385-391
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• 2021

3D modeling of the human body is an integral part of computer graphics. Among them, several studies have been conducted on hair modeling, but there are generally few studies that effectively implement hair and face modeling simultaneously. This study has the originality of providing users with customized face modeling and hair modeling that is different from previous studies. For realistic hair styling, We design and realize hair segmentation using FCN, and we select the most appropriate model through comparing PSPNet, DeepLab V3+, and MobileNet. In this study, we use the open dataset named Figaro1k. Through the analysis of iteration and epoch parameters, we reach the optimized values of them. In addition, we experiment external parameters about the location of the camera, the color of the lighting, and the presence or absence of accessories. And the environmental analysis factors of the avatar maker were set and solutions to problems derived during the analysis process were presented.

• Proceedings of the Korean Society of Computer Information Conference
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• 2022.07a
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• pp.221-222
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• 2022

본 논문에서는 모발 정밀검사(Phototrichogram)를 통해 일정 간격을 두고 촬영된 환자의 모발 두피 사진을 이용하여 머리카락 검출 및 머리카락의 개수 변화 추이에 따른 환자의 탈모 진단에 도움을 줄 방법을 제안하였다. 모발 정밀검사를 진행하여 촬영된 환자의 모발 사진으로부터 딥러닝 기반의 영상 분할 기법(Image Segmentation)의 하나인 DetectoRS 모델을 활용하여 머리카락을 자동 검출한다. 실험 결과 DetectoRS 모델의 분할 성능은 74.74%로 효과적으로 머리카락을 검출하였음을 확인할 수 있었다.

• Proceedings of the Korean Society of Computer Information Conference
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• 2023.01a
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• pp.89-90
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• 2023

본 논문은 모발 정밀검사(Phototrichogram)를 통해 일정 간격을 두고 촬영된 환자의 모발 두피 사진을 이용하여 머리카락 검출 및 개수 변화 추이에 따른 환자의 탈모 진단에 도움을 줄 방법을 제안한다. 기존의 탈모 진단을 위해 제안하였던 머리카락 검출 방법에서 사용한 환자의 모발 두피 사진에 Color Slicing을 적용하여 환자의 두피 모발 사진의 픽셀값을 통일성 있게 구성하였다. 또한, 머리카락 검출하기 위한 방법으로 Swin Transformer를 사용하고, 딥러닝 기반의 영상 분할 기법(Image Segmentation)의 하나인 HTC(Hybrid Task Cascade) 모델을 활용하여 좀 더 효과적으로 머리카락을 검출할 수 있는 모델을 제안한다.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.38 no.6
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• pp.70-81
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• 2001

This paper presents a 3D face modeling method using a CCD camera and a projector (LCD projector or Slide projector). The camera faces the human face and the projector casts white stripe patterns on the human face. The 3D shape of the face is extracted from spatial and temporal locations of the white stripe patterns on a series of image frames. The proposed method employs region segmentation and multi-beam techniques for efficient 3D modeling of hair region and faster 3D scanning respectively. In the proposed method, each image is segmented into face, hair, and shadow regions, which are independently processed to obtain the optimum results for each region. The multi-beam method， which uses a number of equally spaced stripe patterns, reduces the total number of image frames and consequently the overall data acquisition time. Light beam calibration is adopted for efficient light plane measurement, which is not influenced by the direction (vertical or horizontal) of the stripe patterns. Experimental results show that the proposed method provides a favorable 3D face modeling results, including the hair region.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.6
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• pp.2826-2840
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• 2018

Traditional methods based active contours or region merging are powerless in processing images with blurring border or hair occlusion. In this paper, a structure based convolutional neural networks is proposed to solve segmentation of skin lesion image. The structure mainly consists of two networks which are segmentation net and discrimination net. The segmentation net is designed based U-net that used to generate the mask of lesion, while the discrimination net is designed with only convolutional layers that used to determine whether input image is from ground truth labels or generated images. Images were obtained from "Skin Lesion Analysis Toward Melanoma Detection" challenge which was hosted by ISBI 2016 conference. We achieved segmentation average accuracy of 0.97, dice coefficient of 0.94 and Jaccard index of 0.89 which outperform the other existed state-of-the-art segmentation networks, including winner of ISBI 2016 challenge for skin melanoma segmentation.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2003.09a
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• pp.632-635
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• 2003

This paper describes a system fur tracking multiple faces in an input video sequence using facial convex hull based facial segmentation and robust hausdorff distance. The algorithm adapts skin color reference map in YCbCr color space and hair color reference map in RGB color space for classifying face region. Then, we obtain an initial face model with preprocessing and convex hull. For tracking, this algorithm computes displacement of the point set between frames using a robust hausdorff distance and the best possible displacement is selected. Finally, the initial face model is updated using the displacement. We provide an example to illustrate the proposed tracking algorithm, which efficiently tracks rotating and zooming faces as well as existing multiple faces in video sequences obtained from CCD camera.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.3 no.1
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• pp.87-92
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• 2003

This paper describes a system for tracking multiple faces in an input video sequence using facial convex hull based facial segmentation and robust hausdorff distance. The algorithm adapts skin color reference map in YCbCr color space and hair color reference map in RGB color space for classifying face region. Then, we obtain an initial face model with preprocessing and convex hull. For tracking, this algorithm computes displacement of the point set between frames using a robust hausdorff distance and the best possible displacement is selected. Finally, the initial face model is updated using the displacement. We provide an example to illustrate the proposed tracking algorithm, which efficiently tracks rotating and zooming faces as well as existing multiple faces in video sequences obtained from CCD camera.

• Journal of Internet of Things and Convergence
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• v.9 no.3
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• pp.13-19
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• 2023

In this paper, we investigated the implementation of a hairstyle recommendation system based on big data and deepfake technology. The proposed hairstyle recommendation system recognizes the facial shapes based on the user's photo (image). Facial shapes are classified into oval, round, and square shapes, and hairstyles that suit each facial shape are synthesized using deepfake technology and provided as videos. Hairstyles are recommended based on big data by applying the latest trends and styles that suit the facial shape. With the image segmentation map and the Motion Supervised Co-Part Segmentation algorithm, it is possible to synthesize elements between images belonging to the same category (such as hair, face, etc.). Next, the synthesized image with the hairstyle and a pre-defined video are applied to the Motion Representations for Articulated Animation algorithm to generate a video animation. The proposed system is expected to be used in various aspects of the beauty industry, including virtual fitting and other related areas. In future research, we plan to study the development of a smart mirror that recommends hairstyles and incorporates features such as Internet of Things (IoT) functionality.

• Journal of the Society of Cosmetic Scientists of Korea
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• v.30 no.1
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• pp.103-116
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• 2004

The goal of this study was to get the basic data for the domestic cosmetic industry, through analyzing female college ,students’ responses to cosmetic care of hair and their behavior, and their preference for hair cosmetics and brands. In their level of understanding and concern of hair cosmetic care, as well as their general knowledge, ‘dyed or decolorized hair’ types had the highest response rate at 49％, followed by ‘permanent wave’ type at 39％. Thus they were very interested in hairdressing-related operations, and even they made an attempt to do it directly. In the ways and management for dealing with harmful side effects caused by dyeing, decolorizing, or permanent, 74.7％ of the respondents experienced adverse effects, as well as having complaints, after permanent wave. Of the complaints 245 students experienced, ‘hair damage’ was the highest rate of 42.4％, followed by ‘unsatisfied with hair style’ at 19.8％. From the above results, female college students showed a various interest in hair cosmetic care; however, they had little knowledge in this regard. Accordingly, it is necessary to educate them for the segmentation of hair cosmetics and the appropriate hair care, to meet their diversified cosmetic needs which becomes diversified, and to offer them the correct information of hair beauty.