• Journal of the Institute of Electronics and Information Engineers
• /
• v.52 no.8
• /
• pp.106-114
• /
• 2015

Nowadays, cameras embedded in smartphones can take high resolution photographs that can be used to analyze skin conditions without using specialized equipments. In shooting photographs with a smartphone, it is difficult to maintain a uniform shooting distance. Therefore, it is essential to adapt a skin analysis method to the shooting distance. In this paper, we focus on a pore detection algorithm that is adaptive to the camera distance. We develop a relationship model between the shooting distance and the appropriate size of the pore detection mask. In addition, we propose a method to estimate the normalized pore size (i. e. pore size at a standard shooting distance). We conducted experiments on skin images taken from different shooting distances. It was verified that the proposed method can achieve more accurate pore detection result, close to those from skin images taken at a standard shooting distance.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.29 no.9C
• /
• pp.1258-1268
• /
• 2004

We propose a new fast, algorithm which is used for detecting frontal face in the frequency domain based on human skin-color using OCT coefficient of dynamic image compression and skin color information. The region where each pixel has a value of skin-color were extracted from U and V value based on DCT coefficient obtained in the process of Image compression using skin-color map in the Y, U, V color space A morphological filter and labeling method are used to eliminate noise in the resulting image We propose the algorithm to detect fastly human face that estimate the directional feature and variance of luminance block of human skin-color Then Extraction of face was completed adaptively on both background have the object analogous to skin-color and background is simple in the proposed algorithm The performance of face detection algorithm is illustrated by some simulation results earned out on various races We confined that a success rate of 94 % was achieved from the experimental results.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.26 no.2
• /
• pp.120-126
• /
• 2016

In this study, we propose a method for effectively detecting and recognizing the face in image using RBFNNs pattern classifier and HCbCr-based skin color feature. Skin color detection is computationally rapid and is robust to pattern variation for face detection, however, the objects with similar colors can be mistakenly detected as face. Thus, in order to enhance the accuracy of the skin detection, we take into consideration the combination of the H and CbCr components jointly obtained from both HSI and YCbCr color space. Then, the exact location of the face is found from the candidate region of skin color by detecting the eyes through the Haar-like feature. Finally, the face recognition is performed by using the proposed FCM-based RBFNNs pattern classifier. We show the results as well as computer simulation experiments carried out by using the image database of Cambridge ICPR.

• 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.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.11 no.7
• /
• pp.641-648
• /
• 2001

A face detection in color images is important for many multimedia applications. It is first step for face recognition and can be used for classifying specific shorts. This paper describes a new method to detect faces in color images based on the skin color and hair color. This paper presents a fuzzy-based method for classifying skin color region in a complex background under varying illumination. The Fuzzy rule bases of the fuzzy system are generated using training method like a genetic algorithm(GA). We find the skin color region and hair color region using the fuzzy system and apply the convex-hull to each region and find the face from their intersection relationship. To validity the effectiveness of the proposed method, we make experiment with various cases.

• Proceedings of the IEEK Conference
• /
• 2008.06a
• /
• pp.681-682
• /
• 2008

This paper propose skin color detection that uses MLP(Multi Layer Perceptron) and multiple color models. The proposed method reduces weight of MLP by partial connection between input layer and hidden layer based on color models, and the using color models are RGB model and YCbCr model. The experimental result for proposed method showed 94% classification rate of skin and non-skin pixels with 32% decrease in the number of weight compare to general MLP on the average.

• Proceedings of the Korean Society of Computer Information Conference
• /
• 2016.07a
• /
• pp.297-298
• /
• 2016

본 논문에서는 손을 보다 효과적으로 인식하기 위해 동작 검출과 피부색 검출을 이용하여 인식하는 시스템을 제안한다. 단순히 피부색만을 이용하여 손을 인식하는 경우 피부색과 유사한 색상의 물체나 다른 신체 부위를 인식하는 문제점이 발생하게 된다. 이러한 문제점을 해결하기 위해 동작 검출을 이용하여 움직이는 물체만을 손이라고 가정하였다. 이렇게 가정을 하고 피부색 검출과 동작 검출을 이용하여 인식하는 경우 신체부위를 제외하고는 거의 검출되지 않는다. 그리고 인식된 영역마다 뼈대를 찾아 손을 검출한다. 조명이나 주변 환경에 최대한 영향을 적게 받기위해 시스템을 설계하였으며 단순 피부색 검출을 이용한 손 검출보다 좋은 성능을 발휘하며 손가락의 개수와 손 모양, 손 추적까지 응용할 수 있다.

• The Journal of the Institute of Internet, Broadcasting and Communication
• /
• v.15 no.4
• /
• pp.55-61
• /
• 2015

Today, IT technology provide convenience to many people. Smartphone era is opened, and market environment is changing rapidly. Pornography market is active by using smartphone use free internet. Many people access mobile harmful site of USA and Japan. App store of the apple has been cut off the porn service, but access block to mobile Web page is an impossible situation. In this paper, we proposed the harmful image detection method of using skin and non skin features to detect harmful image. Our proposed method can provide enough performance than previous method.

• The Plant Pathology Journal
• /
• v.25 no.3
• /
• pp.291-293
• /
• 2009

The low virus titer in woody plant tissues and the presence of inhibitor compounds such as polyphenols, tannins and polysaccharides are common difficulties that compromise purification of plant viroids from their woody hosts. A simple, reliable method of RNA isolation using CF11 cellulose column on a microcentrifuge tube scale for detecting Apple scar skin viroid (ASSVd) in apple was developed. Total RNA extracted from leaf, woody bark and the fruit skin was used for reverse transcription. RT-PCR products could be detected from RNA prepared from dormant woody bark, fruit skin and fresh leaves with both the CF11 cellulose column method and NucliSens extractor in February, August and November. Meanwhile, with the RNeasy kit RT-PCR, products were detected only in leaves and not from bark or fruit skin. The PCR product, about 330 base pairs, was analyzed by agarose gel electrophoresis. The CF11 cellulose column method was effective for detecting ASSVd. The method enabled the processing of a large numbers of samples of dormant woody bark, leaf and fruit skin of apple.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.37 no.2A
• /
• pp.105-112
• /
• 2012

Face detection at a distance faces is very challenging since images are often degraded by blurring and noise as well as low resolution. This paper proposes an improved face detection method with AdaBoost filtering and sequential testing stages with color and shape information. The conventional AdaBoost filter detects face regions but often generates false alarms. The face detection method is improved by adopting sequential testing stages in order to remove false alarms. The testing stages comprise skin-color test and variable edge-mask filtering. The skin-color filtering is composed of two steps, which involve rectangular window regions and individual pixels to generate binary face clusters. The size of the variable edge-mask is determined by the ellipse which is estimated from the face cluster. The validation of the horizontal and vertical ratio of the mask is also investigated. In the experiments, the efficacy of the proposed algorithm is proved by images captured by a CCTV and a smart-phone