基于YCgCr颜色空间的不良图像肤色检测

针对目前互联网图像内容过滤系统识别率低的情况,提出了一种基于YCgCr空间的不良图像肤色检测方法。首先检测图像中可能存在的人脸区域,利用人脸肤色像素来检测获得人体肤色,其次对不含人脸图像,则利用离线构建的肤色模型来实现肤色检测。实验结果证明,在不同光照以及复杂背景下,该算法能够较好地提高不良图像的肤色检测率和背景检测率。     

一种基于SVM的网络不良图像过滤方法

针对网络不良图像过滤的需求,提出一种基于SVM的不良图片快速过滤方法。该方法利用混合肤色模型实现裸露肤色区域的检测,提取人脸位置、形状和图像背景等特征,组成特征向量。用SVM分类器训练得到检测模型,利用这个模型进行判决,有效提高了不良图片的平均识别率。选取实际网络应用中的正常图像与不良图像,其中不良图像的识别率为83.9%,正常图像的识别率为93.4%,误检率为6.6%,平均识别率达到86.6%,实验显示该方法满足实际应用的需求。     

JPEG图像压缩域上的自适应人体皮肤区域检测算法

快速而准确地检测图像中的人体皮肤区域在人脸检测、敏感图像过滤等应用中有重要价值．为了提高肤色检测的精度和速度，提出了一种在JPEG图像压缩域上的基于自适应闽值的人体皮肤区域检测算法．该算法的优点在于：①检测过程中能根据图像内容自适应地调节检测阈值，有效防止误检和漏检；②无需完全解压缩JPEG图像，直接在DCT系数域上计算每个图像块的颜色和纹理特征，利用颜色和纹理特征来检测图像块是否为人体皮肤．与现有肤色检测算法的对比实验表明我们的方法具有很好的准确性和很快的速度．     

基于内容分析的特定图像过滤技术研究

文章在分析色情图像的特征的基础上,提出基于肤色模型与轮廓特征的图像过滤方法。重点讨论了基于内容的图像过滤方法,其中包括肤色检测、纹理检测、轮廓检测、特征选取和分类方法(Bayes分类器和SVM分类器)等关键技术。实验结果表明,在混合样本的条件下该方法能够达到80%以上的准确率。     

基于人脸检测的网络图像内容过滤技术研究

为了有效防止不良图像的传播,文章提出了一种不良图片内容过滤方法。该方法通过建立一种 RGB、YUV 和 YIQ 空间上的混合肤色模型,实现裸露区域的检测。为有效降低正常图像的误判率,需对整个图像进行人脸检测。通过实验分析,该方法取得了较为理想的实验结果。     

基于人体关键部位检测的敏感图像过滤方法

目前多数敏感图像过滤方法对皮肤裸露较多或类肤色区域较多的图像容易产生误检。为降低对这类图像的误检率,提出一种基于人体关键部位检测的敏感图像过滤方法。该方法提取肤色特征、表征局部对象外观和形状的HOG(Histograms of Orien-ted Gradient)特征、空间分布特征及描述区域灰度分布的Haar-like等特征,利用Adaboost学习算法,训练得到人体关键部位的分类器,通过此分类器实现敏感图像的过滤。实验表明,该方法能够准确地检测关键部位,可以有效地降低敏感图像的误检率。     

基于内容的特定图像过滤方法

针对互联网中色情图像传播愈来愈严重的现象,在充分分析色情图像的特征的基础上,提出基于计算机视觉与模式识别的图像过滤方法,该方法将人体肤色模型、面部模型以及图像轮廓、面积等多项图像特征识别技术相结合,实现网络色情图像识别过滤。该过滤方法,能够有效地过滤掉网络色情图像,实验结果表明,该方法能够达到80％以上的准确率,具有较高的实用性和应用价值。     

融合区域颜色和纹理两级特征的快速人体皮肤检测

人体皮肤检测在人脸检测、成人图像过滤、人体图像检索等应用中占有重要地位.利用支持向量机,对人体皮肤的颜色和纹理特征的分布进行了研究,并提出了一个基于区域颜色和纹理特征规则的两级模型.在人体皮肤检测算法中,首先利用分水岭分割算法将图像分割成颜色和纹理近似一致的区域,然后利用皮肤颜色模型提取候选皮肤区域,最后利用纹理规则模型对候选皮肤区域进行最终判决.实验结果表明,该算法简便、快速、有效.     

基于新颜色空间YCgCr的复杂背景图像下的肤色区域检测

肤色信息在基于彩色图像的手势识别、人脸检测与跟踪和基于内容的不良图像过滤等应用中,起着非常重要的作用.为了有效地检测图像中的肤色区域,采用了类似于YCbCr颜色空间的新颜色空间YCgCr.为了说明YCgCr颜色空间的优越性,给出了该颜色空间与YCbCr颜色空间和Karhunen-Loeve （K-L）变换颜色空间中多样实验操作的比较.实验结果表明,用同样肤色样本得到的肤色阈值对相同的测试图像集进行肤色检测时,YCgCr颜色空间具有很好的肤色区域检测效果,漏检率和误检率均低于其它两个颜色空间的漏检率和误检率,并且对于不同的光照条件有较好的鲁棒性.     

敏感图片自动检测系统的研究

设计并实现了一个敏感图片自动识别系统。该系统首先利用肤色模型从敏感图片中检测出肤色区域,再从图片肤色区域中,提取大量经验特征表示图像内容,然后采用Haar特征算法设计一个检测敏感部位对象的分类器,最后通过敏感部位的几何特征逐级识别正常图片与敏感图片。该检测系统具有良好的系统性能。     

Color based skin classification

Skin detection is used in applications ranging from face detection, tracking body parts and hand gesture analysis, to retrieval and blocking objectionable content. In this paper, we investigate and evaluate (1) the effect of color space transformation on skin detection performance and finding the appropriate color space for skin detection, (2) the role of the illuminance component of a color space, (3) the appropriate pixel based skin color modeling technique and finally, (4) the effect of color constancy algorithms on color based skin classification. The comprehensive color space and skin color modeling evaluation will help in the selection of the best combinations for skin detection. Nine skin modeling approaches (AdaBoost, Bayesian network, J48, Multilayer Perceptron, Naive Bayesian, Random Forest, RBF network, SVM and the histogram approach of Jones and Rehg (2002)) in six color spaces (IHLS, HSI, RGB, normalized RGB, YCbCr and CIELAB) with the presence or absence of the illuminance component are compared and evaluated. Moreover, the impact of five color constancy algorithms on skin detection is reported. Results on a database of 8991 images with manually annotated pixel-level ground truth show that (1) the cylindrical color spaces outperform other color spaces, (2) the absence of the illuminance component decreases performance, (3) the selection of an appropriate skin color modeling approach is important and that the tree based classifiers (Random forest, J48) are well suited to pixel based skin detection. As a best combination, the Random Forest combined with the cylindrical color spaces, while keeping the illuminance component outperforms other combinations, and (4) the usage of color constancy algorithms can improve skin detection performance.     

肤色模型快速人脸检测算法研究

为提高人脸检测效率．研究实现采用肤色模型的低分辨率视频快速人脸检测算法。主要步骤包括采用Gray World方法的图像预处理、图像二值化和形态学处理等,从而实现快速人脸定位。实验结果证明该方法的正确性、快速性和有效性。该算法可以为后期人脸特征检测的精确定位奠定基础。     

Hybrid Human Skin Detection Using Neural Network and K-Means Clustering Technique

Human skin detection is an essential step in most human detection applications, such as face detection. The performance of any skin detection system depends on assessment of two components: feature extraction and detection method. Skin color is a robust cue used for human skin detection. However, the performance of color-based detection methods is constrained by the overlapping color spaces of skin and non-skin pixels. To increase the accuracy of skin detection, texture features can be exploited as additional cues. In this paper, we propose a hybrid skin detection method based on YIQ color space and the statistical features of skin. A Multilayer Perceptron artificial neural network, which is a universal classifier, is combined with the k-means clustering method to accurately detect skin. The experimental results show that the proposed method can achieve high accuracy with an F₁-measure of 87.82% based on images from the ECU database.     

肤色模型的面部肤色区域提取

面部肤色区域提取是数字图像处理和模式识别领域的研究热点。研究了采用OpenCV分类器实现人脸检测的方法,并基于肤色区域的聚类特性,构造出一个肤色模型查找表,进而实现了面部肤色区域的提取。实验证明,这种方法具有良好的适应性和实时性。     

一种改进的敏感图像过滤方法

针对现有敏感图像过滤方法误检率较高的问题,提出一种结合肤色检测和方向梯度直方图(HOG)人体检测的敏感图像过滤方法。采用HOG特征提取人体目标的特征集,运用支持向量机训练人体检测模型,检验图像中是否存在人体,并结合肤色检测算法判别该图像是否为敏感图像。实验结果表明,该方法能有效检测复杂背景条件下的敏感图像,其精确度为90.2%、查全率为86.3%、误检率为3.5%。     

Skin detection by dual maximization of detectors agreement for video monitoring

This paper presents an approach for skin detection which is able to adapt its parameters to image data captured from video monitoring tasks with a medium field of view. It is composed of two detectors designed to get high and low probable skin pixels (respectively, regions and isolated pixels). Each one is based on thresholding two color channels, which are dynamically selected. Adaptation is based on the agreement maximization framework, whose aim is to find the configuration with the highest similarity between the channel results. Moreover, we improve such framework by learning how detector parameters are related and proposing an agreement function to consider expected skin properties. Finally, both detectors are combined by morphological reconstruction filtering to keep the skin regions whilst removing wrongly detected regions. The proposed approach is evaluated on heterogeneous human activity recognition datasets outperforming the most relevant state-of-the-art approaches.     

A survey of skin-color modeling and detection methods

Skin detection plays an important role in a wide range of image processing applications ranging from face detection, face tracking, gesture analysis, content-based image retrieval systems and to various human computer interaction domains. Recently, skin detection methodologies based on skin-color information as a cue has gained much attention as skin-color provides computationally effective yet, robust information against rotations, scaling and partial occlusions. Skin detection using color information can be a challenging task as the skin appearance in images is affected by various factors such as illumination, background, camera characteristics, and ethnicity. Numerous techniques are presented in literature for skin detection using color. In this paper, we provide a critical up-to-date review of the various skin modeling and classification strategies based on color information in the visual spectrum. The review is divided into three different categories: first, we present the various color spaces used for skin modeling and detection. Second, we present different skin modeling and classification approaches. However, many of these works are limited in performance due to real-world conditions such as illumination and viewing conditions. To cope up with the rapidly changing illumination conditions, illumination adaptation techniques are applied along with skin-color detection. Third, we present various approaches that use skin-color constancy and dynamic adaptation techniques to improve the skin detection performance in dynamically changing illumination and environmental conditions. Wherever available, we also indicate the various factors under which the skin detection techniques perform well.     

肤色检测技术综述

肤色检测在人脸和手势识别与跟踪、Web图像内容过滤、数据库或因特网中的人物检索和医疗诊断等方面有广泛应用，文中通过分别介绍基于统计和基于物理的两类肤色检测技术，较全面地综述了肤色检测技术，其中对颜色空间选择、静、动态肤色建模方法、肤色反射模型和肤色波谱特性等肤色检测重要环节做了分析，明确了选择颜色空间与特征提取和分类方法的联系，强调了研究肤色波谱特征对基于物理的肤色检测技术的重要性，最后探讨了肤色检测的技术难题和发展趋势。