• Journal of the Korea Society of Computer and Information
• /
• v.26 no.6
• /
• pp.173-181
• /
• 2021

Chinese people have increasingly high interests in skin care and trust and prefer medical institution products and equipment to treat skin problems. The purpose of this study is to examine skin types and skin care situations, skin care recognition, and skin care satisfaction by gender in medical skin care center patients from in their 10s to 50s in Hainan Province, China. The questionnaire survey consisted of general characteristics(n=8), skin care situations(n=6), skin care recognition(n=11), and skin care satisfaction(n=21). A total of 328 questionnaires were researched from December 21, 2020 to January 9, 2021 using WeChat and Wenjuanxing program. Data were analyzed by SPSSWIN 21.0. Frequency analysis was applied for general characteristics, skin care situations, skin care recognition, and skin care satisfaction and Cronbach's α was used for the reliability of skin care recognition and satisfaction. The relationship among skin care situations, skin care recognition, and skin care satisfaction was analyzed by χ² test and t-test. As a result, the common skin types by gender was dry skin in females and oily skin in males. The highest skin trouble was melasma and pigments in females and pimple in males. The most common way to manage troubled skin was homecare in both females and males, followed by the dermatology department in females and pharmacy in males, suggesting a significant difference. The common period of skin trouble was from one year to three years and the most effective way to improve skin was good life habit, followed by laser treatment in both females and males. The most important consideration to choose a hospital was a famous franchise hospital and the most important matters in management was doctor or skin care professionalism. Skin care and treatment recognition was high in external effects for females and internal effects for males. Skin care satisfaction was high in service for females and effect for males. Skin care satisfaction was significantly higher in males than in females. In conclusion, there was a difference in skin types, skin troubles, skin problems, skin care ways, and skin care satisfaction by gender in Chinese medical skin care center patients. Therefore, this study suggests the development of various products and the need of systematic management programs.

• Journal of Digital Convergence
• /
• v.16 no.10
• /
• pp.465-477
• /
• 2018

The purpose of this study is how it affect them what the effect of skin recognition on the purchasing behavior and propensity to buy facial cleanser in 311 women in their 20s and 50s. This study analyzed by importance, interest, and satisfaction of skin recognition, and type of impulse buying, type of depending on brand, type of planning buying. The group with high interest in skin recognition had a long time to clean. As they got a purchasing information the group with low knowledge had the information from nearby, and the group with high knowledge got information from internet. At the view of purchasing propensity the women who are highly interested in the skin have a tendency of type of impulse buying and type of planning buying, and the women with high skin importance are less inclined to type of impulse buying. In conclusion, Skin recognition uses purchasing behavior and propensity to buy facial cleanser.

• Journal of Korea Multimedia Society
• /
• v.25 no.8
• /
• pp.1212-1223
• /
• 2022

Skin lesions are common diseases that range from skin rashes to skin cancer, which can lead to death. Note that early diagnosis of skin diseases can be important because early diagnosis of skin diseases considerably can reduce the course of treatment and the harmful effect of the disease. Recently, the development of computer-aided diagnosis (CAD) systems based on artificial intelligence has been actively made for the early diagnosis of skin diseases. In a typical CAD system, the accurate classification of skin lesion types is of great importance for improving the diagnosis performance. Motivated by this, we propose a novel deep ensemble classification with multi-scale attention networks. The proposed deep ensemble networks are jointly trained using a single loss function in an end-to-end manner. In addition, the proposed deep ensemble network is equipped with a multi-scale attention mechanism and segmentation information of the original skin input image, which improves the classification performance. To demonstrate our method, the publicly available human skin disease dataset (HAM 10000) and the private animal skin lesion dataset were used for the evaluation. Experiment results showed that the proposed methods can achieve 97.8% and 81% accuracy on each HAM10000 and animal skin lesion dataset. This research work would be useful for developing a more reliable CAD system which helps doctors early diagnose skin diseases.

• The KIPS Transactions:PartB
• /
• v.9B no.4
• /
• pp.491-500
• /
• 2002

In this paper, we propose a real-time face detection and recognition system by using skin color informations, geometrical feature vectors of face, and facial angle informations from color face image. The proposed algorithm improved face region extraction efficiency by using skin color informations on the HSI color coordinate and face edge information. And also, it improved face recognition efficiency by using geometrical feature vectors of face and facial angles from the extracted face region image. In the experiment, the proposed algorithm shows more improved recognition efficiency as well as face region extraction efficiency than conventional methods.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.9 no.5
• /
• pp.1856-1869
• /
• 2015

This paper addresses the issues of 3D human activity detection, tracking and recognition from RGB-D video sequences using a feature structured framework. During human tracking and activity recognition, initially, dense depth images are captured using depth camera. In order to track human silhouettes, we considered spatial/temporal continuity, constraints of human motion information and compute centroids of each activity based on chain coding mechanism and centroids point extraction. In body skin joints features, we estimate human body skin color to identify human body parts (i.e., head, hands, and feet) likely to extract joint points information. These joints points are further processed as feature extraction process including distance position features and centroid distance features. Lastly, self-organized maps are used to recognize different activities. Experimental results demonstrate that the proposed method is reliable and efficient in recognizing human poses at different realistic scenes. The proposed system should be applicable to different consumer application systems such as healthcare system, video surveillance system and indoor monitoring systems which track and recognize different activities of multiple users.

• Journal of Electrical Engineering and Technology
• /
• v.11 no.6
• /
• pp.1825-1838
• /
• 2016

The detection of skin pigment is crucial in the diagnosis of skin diseases and in the evaluation of medical cosmetics and hairdressing. Accuracy in the detection is a basis for the prompt cure of skin diseases. This study presents a method to recognize and measure human skin pigment using Hemoglobin-Melanin (HM) coordinate. The proposed method extracts the skin area through a Gaussian skin-color model estimated from statistical analysis and decomposes the skin area into two pigments of hemoglobin and melanin using an Independent Component Analysis (ICA) algorithm. Then, we divide the two-dimensional (2D) HM coordinate into rectangular bins and compute the location histograms of hemoglobin and melanin for all the bins. We label the skin pigment of hemoglobin, melanin, and normal skin on all bins according to the Bayesian classifier. These bin-based HM projective histograms can quantify the skin pigment and compute the standard deviation on the total quantification of skin pigments surrounding normal skin. We tested our scheme using images taken under different illumination conditions. Several cosmetic coverings were used to test the performance of the proposed method. The experimental results show that the proposed method can detect skin pigments with more accuracy and evaluate cosmetic covering effects more effectively than conventional methods.

• Journal of the Korea Fashion and Costume Design Association
• /
• v.16 no.1
• /
• pp.77-89
• /
• 2014

This study looked into the effect of the life style of single females in 20s and 30s on beauty behavior recognition, and spss 17.0 is used for data analysis method. As for the statistical analysis method in order to validate the measurement tools, reliability verification is conducted and life style groups are sampled using K-means taking into account factor scores by life style. To find out the difference between general beauty behavior recognition and life style, descriptive statistics and One Way ANOVA were carried out, and Duncan Test was implemented for the post examination method. Multiple regression analysis was also carried out to figure out the effect of life style on beauty behavior recognition. The result is as follows. First, according to the results of reliability verification and factor analysis for the lifestyle type and the recognition of the behavior for beauty, the types of the life style of the subjects were divided into Economic Utility, Convention Conservatism, Self Development, Showy Consumption, and Appearance Oriented, and the recognition of the behavior for beauty was named as Makeup and Hair, Cosmetic Surgery, Body Care, and Skin Care. Second, as to the recognition of the behavior for beauty based upon the lifestyle, the Appearance Oriented in Showy Consumption recorded the highest. Third, the analysis of the influence of the style on the recognition of the behavior for beauty showed that the behavior recognition for Makeup and Hair and for Skin Care was affected by the life style of Self Development, Showy Consumption, and Appearance Oriented; the behavior recognition for Cosmetic Surgery was affected by the life style of Conventional Conservatism, Showy Consumption, and Appearance Oriented; and again the behavior recognition for Body Care was by that of Economical Utility and Showy Consumption.

• KIPS Transactions on Software and Data Engineering
• /
• v.10 no.12
• /
• pp.555-560
• /
• 2021

Recently, as people's interest in facial skin beauty has increased, research on skin disease recognition for facial skin beauty is being conducted by using deep learning. These studies recognized a variety of skin diseases, including acne. Existing studies can recognize only the single skin diseases, but skin diseases that occur on the face can enact in a more diverse and complex manner. Therefore, in this paper, complex skin diseases such as acne, blackheads, freckles, age spots, normal skin, and whiteheads are identified using the Inception-ResNet V2 deep learning mode with multi-label classification. The accuracy was 98.8%, hamming loss was 0.003, and precision, recall, F1-Score achieved 96.6% or more for each single class.

• Journal of Korea Multimedia Society
• /
• v.14 no.7
• /
• pp.866-871
• /
• 2011

In this paper, an improved method of hand detecting and hand gesture recognition is proposed, it can be applied in different illumination condition and complex background. We use Adaptive Skin Threshold (AST) to detect the areas of hand. Then the result of hand detection is used to hand recognition through the improved HMM algorithm. At last, we design a simple program using the result of hand recognition for recognizing "stone, scissors, cloth" these three kinds of hand gesture. Experimental results had proved that the hand and gesture can be detected and recognized with high average recognition rate (92.41%) and better than some other methods such as syntactical analysis, neural based approach by using our approach.

• Journal of Industrial Technology
• /
• v.29 no.A
• /
• pp.47-54
• /
• 2009

In this paper, we proposed hand detection and hand gesture recognition from USB camera video. Firstly, we extract hand region extraction using skin color information from a difference images. Background image is initially stored and extracted from the input images in order to reduce problems from complex backgrounds. After that, 16-directional chain code sequence is computed from the tracking of hand motion. These chain code sequences are compared with pre-trained models using DP matching. Our hand gesture recognition system can be used to control PowerPoint slides or applied to multimedia education systems. We got 92% hand region extraction accuracy and 82.5% gesture recognition accuracy, respectively.