• 한국컴퓨터정보학회논문지
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• 제26권6호
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• pp.173-181
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• 2021

본 연구에서는 중국 하이난성 지역 10대~50대 남녀를 대상으로 메디컬스킨케어센터를 내원한 환자들을 중심으로 성별에 따른 피부유형 및 피부관리실태, 피부관리 인지도, 만족도를 검증하고자 한다. 이에 피부관리 실태, 피부관리 인지도, 피부관리 만족도 등을 2020년 12월 21일에서 2021년 1월 9일까지 위쳇(WeChat), 왠쥬엔씽 프로그램(wenjuanxing program)을 이용하여 총 328부를 조사하여 SPSSWIN 21.0 프로그램을 사용하여 분석하였다. 피부유형 및 피부관리실태, 피부관리 인지도 및 만족도는 빈도분석(Frequency Analysis)을 실시하였고, 피부관리 인지도와 만족도 신뢰도는 Cronbach's α의 계수를 구하였다. 성별에 따른 피부유형 및 피부관리실태, 인지도, 만족도의 관련성은 카이스케어 검정(χ²)과 t-test를 실시하였다. 분석결과 성별에 따른 피부타입은 여성은 건성피부, 남성은 지성피부이고, 피부고민은 여성은 기미색소, 남성은 여드름피부로 성별에 따라 차이가 나타났다. 이러한 문제성피부관리는 남녀모두 홈케어가 높았고, 다음으로 여성은 피부과, 남성은 약국으로 유의미한 차이를 나타내었다. 진행기간은 남녀모두 1~3년 미만이고, 효과적인 피부 개선 방법으로는 남녀모두 좋은 생활습관, 레이저 순이었다. 병원 선택 시 고려 사항으로는 유명한 체인병원이고, 관리 시 중요 사항은 의사나 피부관리의 전문성을 고려한 것으로 응답하였다. 피부관리 및 치료 인지도는 여성은 외적, 남성은 내적이 높았고, 피부관리 만족도의 차이는 여성은 서비스, 남성은 효과로 나타났으며, 관리만족도는 남성이 여성보다 유의미하게 더 높은 것으로 나타났다. 결론은 중국 메디컬스킨케어센터를 내원한 환자들이 성별에 따라 피부유형 및 피부고민, 피부문제, 피부관리 방법, 피부관리 만족도에 차이가 있는 것으로 분석되어 다양한 제품 개발 및 체계적인 관리프로그램의 필요성을 제시하였다.

• 디지털융복합연구
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• 제16권10호
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• pp.465-477
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• 2018

본 연구는 20-50대 여성 311명을 대상으로 피부인식이 세안제 구매행동 및 구매성향에 미치는 영향을 연구하고자 피부인식을 중요도, 관심도, 만족도로, 구매성향을 충동구매형, 브랜드의존형, 계획구매형으로 하위요인을 구성하여 설문조사를 통하여 분석하였다. 피부인식에서 관심도가 높은 그룹이 세안시간이 길었으며 피부지식도가 낮은 그룹은 구매정보를 주변으로부터, 높은 그룹은 인터넷에서 얻는 것으로 나타났다. 구매성향에서 피부관심도가 높은 여성들은 충동구매형과 계획구매형 성향이 강하고 피부중요도가 높은 여성은 충동구매형 성향이 낮은 것으로 나타났다. 결론적으로 피부 관심도, 중요도 인식이 세안제 구매행동 및 구매성향에 영향을 미치기 때문에 고객의 피부인식을 파악하여 세안제 마케팅에 적용하는 것이 필요할 것으로 사료된다.

• 한국멀티미디어학회논문지
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• 제25권8호
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• pp.1212-1223
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• 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.

• 정보처리학회논문지B
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• 제9B권4호
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• pp.491-500
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• 2002

본 논문에서는 칼라 얼굴 영상으로부터 피부색 정보, 얼굴의 기하학적 특징벡터 및 안면각 정보를 이용한 실시간 얼굴검출 및 인식 알고리즘을 제안하였다. 제안한 알고리즘에서는 HSI 칼라좌표계상의 얼굴 피부색 정보와 얼굴 에지 정보를 함께 이용함으로써 얼굴 영역 검출 효율을 개선하였다. 또한 추출된 얼굴 영역으로부터 얼굴인식율 개선을 위해 얼굴 특징자들을 추출하고 추출된 얼굴 특징자들의 기하학적 관계로 구성된 얼굴 특징벡터와 얼굴 안면각 정보를 사용하여 얼굴 인식율을 개선하였다. 실험에서는 제안한 방법이 기존의 방법에 비해 얼굴 영역 검출율 뿐만 아니라 얼굴 인식율도 개선되었음을 알 수 있다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제9권5호
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• pp.1856-1869
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• 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
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• 제11권6호
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• pp.1825-1838
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• 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.

• 한국의상디자인학회지
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• 제16권1호
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• pp.77-89
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• 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.

• 정보처리학회논문지:소프트웨어 및 데이터공학
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• 제10권12호
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• pp.555-560
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• 2021

최근 안면 피부 미용에 대한 사람들의 관심이 높아짐에 따라 딥 러닝을 활용한 안면 피부 미용을 위한 피부 질환 인식 연구가 진행되고 있다. 이러한 연구들은 여드름을 비롯한 다양한 피부 질환을 인식한다. 기존의 연구들은 단일 피부 질환만을 인식하지만, 안면에 발생하는 피부 질환은 더 다양하고 복합적으로 발생할 수 있다. 따라서 본 논문에서는 Inception-ResNet V2 모델을 활용하여 다중 레이블 분류 방법으로 여드름, 블랙헤드, 주근깨, 검버섯, 일반 피부, 화이트헤드에 관한 복합적인 피부 질환을 인식한다. 사용한 평가 지표 중 정확도는 98.8%, 해밍 손실은 0.003을 달성하였고, 단일 클래스별 정밀도, 재현율, F1-점수는 모두 96.6% 이상을 달성하였다.

• 한국멀티미디어학회논문지
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• 제14권7호
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• pp.866-871
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• 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.

• 산업기술연구
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• 제29권A호
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• pp.47-54
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• 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.