• Title/Summary/Keyword: 피부색 분포도

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Adult Contents Filtering Technique using Image and Sound (사운드와 이미지를 기반으로 한 성인 컨텐츠 필터링 기법)

  • Cho, Jungik;Jo, Jinsu;Lee, Yillbyung
    • Proceedings of the Korea Information Processing Society Conference
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    • pp.121-123
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    • 2007
  • 현재까지 유해한 컨텐츠(Contents)를 차단하기 위한 활발한 연구가 있었으나, 사람의 사운드(sound)와 이미지(image)를 통합한 필터링(filtering) 기법에 대한 연구는 활발히 이루어지지 않은 측면이 있다. 본 논문은 이미지(image) 데이터 중 피부색 분포 비율과 사운드(sound) 데이터 중 주파수 분석을 통한 심층적인 기법을 활용하여 현재까지 진행되고 있는 이미지 필터링(image filtering)방법에 대한 수행 결과보다 획기적으로 개선된 성능을 보이고자 한다. 즉, 사운드와 이미지의 특징 정보를 이용한 성인 컨텐츠(Adult Contents)분류 기법을 활용하는 것으로 성인 컨텐츠(Adult Contents)에서 두드러지는 특징을 보이는 사운드 패턴을 분석하여 현재까지 한정된 자원인 이미지만을 활용한 기법보다는 현저한 향상된 수행능력을 예측해 볼 수 있다.

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Face Detection for Intelligent Video Conference System (지능형 영상회의를 위한 얼굴검출)

  • Park, Jae-Hyeon;Park, Gyu-Sik;On, Seung-Yeop;Kim, Cheon-Guk
    • The KIPS Transactions:PartB
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    • v.8B no.1
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    • pp.20-27
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    • 2001
  • 얼굴검출은 현재 많은 연구가 활발히 진행되고 있는 분야로 보안, 인식 등 다양한 응용분야를 갖는다. 본 논문은 카메라가 화자의 이동에 따라 이를 추적하여 회전하고 회의상황에 맞는 앵글을 유지하는 지능형 영상회의 시스템 개발의 기본요소인 화자검출의 선행단계로 얼굴검출에 대한 새로운 방법을 제안한다. RGB 색 공간의 입력영상을 YIQ 공간으로 변환한 후 IQ 성분은 피부영역검출에 Y 성분은 얼굴의 특성을 추출하는데 사용된다. 색 분포도를 이용하여 피부영역을 검출하고, 마스크를 누적 적용하여 잡음을 제거한 후 얼굴의 구조적인 특성과 명암의 분포를 이용하여 얼굴영역이 검출된다. 실험결과 다양한 배경의 영상에서 여러 명의 얼굴이 오류 없이 검출됨이 관찰되었다.

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A Study on Facial Pose Estimation using TSL Color Information and Geometrical Structure (TSL 색상 정보와 기하학적 구조를 이용한 얼굴 포즈 추정에 관한 연구)

  • 김성환;채재영;김낙빈
    • Proceedings of the Korea Multimedia Society Conference
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    • pp.285-289
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    • 2003
  • 본 논문은 컬러 입력 영상에서 검출된 얼굴 영역 내의 홀(hole)들간의 기하학적 구조를 이용하여 포즈를 추정하는 방법을 제시한다. 얼굴 영역 검출에서는 특징값 기반의 알고리즘 중 피부색 분포를 이용하는 방법을 적용하며, 이 때 발생하는 조명에 의한 열화를 제거한다. 본 논문에서는 TSL 색상 모델을 사용하고, 조명에 의해 너무 밝게 표현되는 부분의 피부값을 조정함으로써 조명에 대한 보정을 실시한다. 그런 다음, 얼굴 영역 안에서 찾은 홀을 피부영역이 아닌 얼굴 구성요소(양눈, 입)로 가정하여, 후보 구성요소들의 기하학적 구조를 이용해 다양한 포즈의 입력 영상에 대한 포즈를 추정한다. 추정된 값은 향후 다양한 포즈에 대한 특징점 추출이나 얼굴 인식에 활용될 수 있다.

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2-Stage Adaptive Skin Color Model for Effective Skin Color Segmentation in a Single Image (단일 영상에서 효과적인 피부색 검출을 위한 2단계 적응적 피부색 모델)

  • Do, Jun-Hyeong;Kim, Keun-Ho;Kim, Jong-Yeol
    • 한국HCI학회:학술대회논문집
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    • pp.193-196
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    • 2009
  • Most of studies adopt a fixed skin color model to segment skin color region in a single image. The methods, however, result in low detection rates or high false positive error rates since the distribution of skin color is varies depending on the characteristics of input image. For the effective skin color segmentation, therefore, we need a adaptive skin color model which changes the model depending on the color distribution of input image. In this paper, we propose a novel adaptive skin color segmentation algorithm consisting of 2 stages which results in both high detection rate and low false positive error rate.

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Face Detection Based on Distribution Map (분포맵에 기반한 얼굴 영역 검출)

  • Cho Han-Soo
    • Journal of Korea Multimedia Society
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    • v.9 no.1
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    • pp.11-22
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    • 2006
  • Recently face detection has actively been researched due to its wide range of applications, such as personal identification and security systems. In this paper, a new face detection method based on the distribution map is proposed. Face-like regions are first extracted by applying the skin color map with the frequency to a color image and then, possible eye regions are determined by using the pupil color distribution map within the face-like regions. This enables the reduction of space for finding facial features. Eye candidates are detected by means of a template matching method using weighted window, which utilizes the correlation values of the luminance component and chrominance components as feature vectors. Finally, a cost function for mouth detection and location information between the facial features are applied to each pair of the eye candidates for face detection. Experimental results show that the proposed method can achieve a high performance.

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Face Region Extraction using Object Unit Method (객체 단위 방법을 사용한 얼굴 영역 추출)

  • 선영범;김진태;김동욱;이원형
    • Journal of Korea Multimedia Society
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    • v.6 no.6
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    • pp.953-961
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    • 2003
  • This paper suggests an efficient method to extract face regions from the com]]lex background. Input image is transformed to color space, where the data is independent of the brightness and several regions are extracted by skin color information. Each extracted region is processed as an object. Noise and overlapped objects ate removed. The candidate objects, faces are likely to be included in, are selected by checking the sizes of extracted objects, the XY ratio, and the distribution ratio of skin colors. In this processing, the objects without face are excluded out of candidate regions. The proposed method can be applied for successful extraction of face regions under various conditions such as face extraction with complex background, slanted faces, and face with accessories, etc.

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Face Detection based on Pupil Color Distribution Maps with the Frequency under the Illumination Variance (빈도수를 고려한 눈동자색 분포맵에 기반한 조명 변화에 강건한 얼굴 검출 방법)

  • Cho, Han-Soo
    • The Journal of the Institute of Internet, Broadcasting and Communication
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    • v.9 no.5
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    • pp.225-232
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    • 2009
  • In this paper, a new face detection method based on pupil color distribution maps with the frequency under the illumination variance is proposed. Face-like regions are first extracted by applying skin color distribution maps to a color image and then, they are reduced by using the standard deviation of chrominance components. In order to search for eye candidates effectively, the proposed method extracts eye-like regions from face-like regions by using pupil color distribution maps. Furthermore, the proposed method is able to detect eyes very well by segmenting the eye-like regions, based on a lighting compensation technique and a segmentation algorithm even though face regions are changed into dark-tone due to varying illumination conditions. Eye candidates are then detected by means of template matching method. Finally, face regions are detected by using the evaluation values of two eye candidates and a mouth. Experimental results show that the proposed method can achieve a high performance.

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Integrated 3D Skin Color Model for Robust Skin Color Detection of Various Races (강건한 다인종 얼굴 검출을 위한 통합 3D 피부색 모델)

  • Park, Gyeong-Mi;Kim, Young-Bong
    • The Journal of the Korea Contents Association
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    • v.9 no.5
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    • pp.1-12
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    • 2009
  • The correct detection of skin color is an important preliminary process in fields of face detection and human motion analysis. It is generally performed by three steps: transforming the pixel color to a non-RGB color space, dropping the illuminance component of skin color, and classifying the pixels by the skin color distribution model. Skin detection depends on by various factors such as color space, presence of the illumination, skin modeling method. In this paper we propose a 3d skin color model that can segment pixels with several ethnic skin color from images with various illumination condition and complicated backgrounds. This proposed skin color model are formed with each components(Y, Cb, Cr) which transform pixel color to YCbCr color space. In order to segment the skin color of several ethnic groups together, we first create the skin color model of each ethnic group, and then merge the skin color model using its skin color probability. Further, proposed model makes several steps of skin color areas that can help to classify proper skin color areas using small training data.

A Face Detection Method using Gradual Expansion of Skin Color Range (피부색 범위의 점진적 확장에 의한 얼굴 검출 방법)

  • 문대성;한영미;김민환
    • Journal of Korea Multimedia Society
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    • v.4 no.5
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    • pp.396-405
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    • 2001
  • Usually it is difficult to extract facial regions in a complex image by using only a predetermined skin color. Expecially, it is more difficult to separate them from background regions that contains the skin color. This paper proposes a face detection method by using gradual range expansion of an initial skin color. By analyzing the skin color distribution several images that are collected in the Web, the range of dense distribution is selected as the range of the initial skin color. In each expanding step, expanded regions in the image are tested whether they can be actual facial regions by using the information of the shape of general face and the location of face organs. The shape of general face is modeled as an ellipse and the aspect ratio of its bounding box is used to define the shape constraint for faces. Only the eyes and lips are used as the face organs, which can be easily detected by extracting horizontal edges in the expanded regions. through several experiments, it is confirmed that the proposed method can detect exactly not only faces having partly distorted regions by highlight but also faces neighboring similar color regions.

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Skin Color Detection Using Partially Connected Multi-layer Perceptron of Two Color Models (두 칼라 모델의 부분연결 다층 퍼셉트론을 사용한 피부색 검출)

  • Kim, Sung-Hoon;Lee, Hyon-Soo
    • Journal of the Institute of Electronics Engineers of Korea CI
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    • v.46 no.3
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    • pp.107-115
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    • 2009
  • Skin color detection is used to classify input pixels into skin and non skin area, and it requires the classifier to have a high classification rate. In previous work, most classifiers used single color model for skin color detection. However the classification rate can be increased by using more than one color model due to the various characteristics of skin color distribution in different color models, and the MLP is also invested as a more efficient classifier with less parameters than other classifiers. But the input dimension and required parameters of MLP will be increased when using two color models in skin color detection, as a result, the increased parameters will cause the huge teaming time in MLP. In this paper, we propose a MLP based classifier with less parameters in two color models. The proposed partially connected MLP based on two color models can reduce the number of weights and improve the classification rate. Because the characteristic of different color model can be learned in different partial networks. As the experimental results, we obtained 91.8% classification rate when testing various images in RGB and CbCr models.