Skin Region Detection Research Articles

Fuzzy system-based real-time face tracking in a multi-subject environment with a pan-tilt-zoom camera

This paper proposes real-time face tracking in a multi-subject environment with a pan-tilt-zoom camera using the fuzzy system technique. Tracking is based on detected faces in the Hue–Saturation–Value (HSV) color space. To detect faces, a fuzzy classifier segments skin colors in the HS color space. To reduce the influence of illumination, a fuzzy system is designed to adaptively determine the fuzzy classifier segmentation threshold according the V color space of an image. Detected skin regions are considered face candidates. Shape and color features serve as another fuzzy classifier inputs, leading to a final detection. For face tracking, a Kalman filter algorithm predicts face detection regions and corrects tracking trajectory. When no face is detected, the consecutive frame difference is employed to track the moving person to avoid tracking lost. To track a specific person among multiple persons, a clothing color histogram in the HS space serves as the determination criterion. The performance of the proposed face detection method is compared to other real-time face detection methods. In real-time operations, the tracking system uses camera panning, tilting, and zooming operations to keep the tracked person within the camera view and maintain a suitable face size in the image.

Detection of skin region from multiband near‐IR spectral characteristics

AbstractMany active safety technologies for the driver support system are developing. Most traffic accidents are caused by the driver's inattentiveness or drowsiness. We are developing a driver support system that protects against traffic accidents due to these causes. Our purpose is to detect the driver's face region with a camera. Many face detection methods are proposed, but there is no technique addressing every environment inside the car. For example, skin color segmentation cannot detect the skin region in the night, because it has to light up the driver by bright light. In this paper, we propose a skin detection method using the unique reflection characteristics of the materials. Our method is a very simple algorithm. We developed a skin detection system, and confirmed its effectiveness by an evaluation experiment in an indoor environment, and showed its effectiveness by a nighttime driving experiment. © 2009 Wiley Periodicals, Inc. Electron Comm Jpn, 92(11): 19–27, 2009; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/ecj.10149

A Skin Detection Approach Based on Color Distance Map

We propose a reliable approach to detect skin regions that can be used in various human-related image processing applications. We use a color distance map, which itself is a grayscale image making the process simple, but still containing color information. Based on this map, we generate some skin as well as nonskin seed pixels, and then grow them to capture the appropriate regions. This approach outperforms the existing approaches in terms of segmenting solid and perfect skin regions. It does not generate much noisy segments. Moreover, it does not need any prior training session and can adapt to detect skin regions from images taken at different imaging conditions.

A human-machine cooperative system for generating sign language animation using thermal image

We propose a new approach aimed at sign language animation by skin region detection on an infrared image. To generate several kinds of animations expressing personality and/or emotion appropriately, conventional systems require many manual operations. However, a promising way to realize a lower workload is to manually refine an animation made automatically with a dynamic image of real motion. In the proposed method, a 3D CG model corresponding to a characteristic posture in sign language is made automatically by pattern recognition on a thermal image, and then a person’s hand in the CG model is set. The hand part is made manually beforehand. If necessary, the model can be replaced manually by a more appropriate model corresponding to training key frames and/or the model can be refined manually. In our experiments, a person experienced in using sign language recognized the Japanese sign language of 71 words expressed as animation with 88.3% accuracy, and three persons experienced in using sign language also recognized the sign language animation representing three emotions (neutral, happy and angry) with 88.9% accuracy.

이질적 템플릿 매칭의 융합을 이용한 얼굴 영역 검출

본 논문은 영상에 포함된 얼굴을 보다 빠르고 강건하게 검출하기 위해서 이질적 템플릿 매칭의 결과들을 융합하여 얼굴을 검출하는 방법을 제안한다. 먼저 광범위한 조명 환경과 인종을 포괄하는 피부색 모델을 이용해 피부 영역을 검출한다. 그리고 영역 라벨링과 필터링으로 매칭에 필요한 검색 범위를 줄인 후, 피부색과 에지를 이용한 템플릿 매칭을 검출된 영역에 적용한다. 이들 매칭 결과가 융합되어 두 매칭 결과를 동시에 최적으로 만족하는 얼굴이 검출된다. 실험 결과는 제안된 방법이 단일 템플릿을 적용할 때보다 얼굴색과 유사한 배경에서 얼굴을 강건하게 검출하며, 얼굴 후보 영역으로 검색 범위를 줄여 검출 시간을 줄였음을 보여준다. 또한 전역 누산기를 사용하여 템플릿 매칭의 과도한 공간 요구의 문제점을 해결할 수 있었다. For fast and robust face detection, this paper proposes an approach for face detection using fusion of heterogeneous template matching. First, we detect skin regions using a model of skin color which covers various illumination and races. After reducing a search space by region labelling and filtering, we apply template matching with skin color and edge to the detected regions. Finally, we detect a face by finding the best choice of template fusion. Experimental results show the proposed approach is more robust in skin color-like environments than with a single template matching and is fast by reducing a search space to face candidate regions. Also, using a global accumulator can reduce excessive space requirements of template matching.

Self-Adaptive Image Cropping for Small Displays

We propose a new self-adaptive image cropping algorithm where the processing steps are driven by the classification of the images into semantic classes. The algorithm exploits both visual and semantic information. Visual information is obtained from a visual attention model, while semantic information relates to the automatically assigned image genre and to the detection of face and skin regions.

NEURAL NETWORK APPROACH FOR IMAGE CHROMATIC ADAPTATION FOR SKIN COLOR DETECTION

The goal of image chromatic adaptation is to remove the effect of illumination and to obtain color data that reflects precisely the physical contents of the scene. We present in this paper an approach to image chromatic adaptation using Neural Networks (NN) with application for detecting--adapting human skin color. The NN is trained on randomly chosen color images containing human subject under various illuminating conditions, thereby enabling the model to dynamically adapt to the changing illumination conditions. The proposed network predicts directly the illuminant estimate in the image so as to adapt to human skin color. The comparison of our method with Gray World, White Patch and NN on White Patch methods for skin color stabilization is presented. The skin regions in the NN stabilized images are successfully detected using a computationally inexpensive thresholding operation. We also present results on detecting skin regions on a data set of test images. The results are promising and suggest a new approach for adapting human skin color using neural networks.

Practical Considerations to Skin Detection Scheme

Detecting skin regions of an image is one of under interest areas in computer vision and graphics. It can be primary step in several applications like advanced human-computer interaction, biometric authentication or contextual image retrieval. Several different researches and challenges have been done to classify the image regions into two groups of skin and non-skin areas. Most of them discussed well in theoretical aspects like effect of different color spaces or classifiers on large datasets. Here it is tried to have practical look to the problem. In a real final system we will have some limitations on speed, memory and even training dataset in different conditions. After a short overview to some common previous methods, a novel skin model is proposed based on LVQ neural networks. Consequently a study on homogenization methods gives more accurate choice for this step. It is shown with the experiments that this scheme can reserve advantages of several different methods at once. It can perform comparable accuracy of 87.92% with acceptable speed while covering wide variety of skin color with a limited size of training data.

SHAPE-BASED ADULT IMAGE DETECTION

This paper investigates adult images detection based on the shape features of skin regions. In order to accurately detect skin regions, we propose a skin detection method using multi-Bayes classifiers in the paper. Based on skin color detection results, shape features are extracted and fed into a boosted classifier to decide whether or not the skin regions represent a nude. We evaluate adult image detection performance using different boosted classifiers and different shape descriptors. Experimental results show that classification using boosted C4.5 classifier and combination of different shape descriptors outperforms other classification schemes.

Practical Considerations to Skin Detection Scheme

Detecting skin regions of an image is one of under interest areas in computer vision and graphics. It can be primary step in several applications like advanced human-computer interaction, biometric authentication or contextual image retrieval. Several different researches and challenges have been done to classify the image regions into two groups of skin and non-skin areas. Most of them discussed well in theoretical aspects like effect of different color spaces or classifiers on large datasets. Here it is tried to have practical look to the problem. In a real final system we will have some limitations on speed, memory and even training dataset in different conditions. After a short overview to some common previous methods, a novel skin model is proposed based on LVQ neural networks. Consequently a study on homogenization methods gives more accurate choice for this step. It is shown with the experiments that this scheme can reserve advantages of several different methods at once. It can perform comparable accuracy of 87.92% with acceptable speed while covering wide variety of skin color with a limited size of training data.

Face detection in color images

Human face detection plays an important role in applications such as video surveillance, human computer interface, face recognition, and face image database management. We propose a face detection algorithm for color images in the presence of varying lighting conditions as well as complex backgrounds. Based on a novel lighting compensation technique and a nonlinear color transformation, our method detects skin regions over the entire image and then generates face candidates based on the spatial arrangement of these skin patches. The algorithm constructs eye, mouth, and boundary maps for verifying each face candidate. Experimental results demonstrate successful face detection over a wide range of facial variations in color, position, scale, orientation, 3D pose, and expression in images from several photo collections (both indoors and outdoors).