基于人体关键部位检测的网上敏感图片过滤技术研究
摘要
随着网络技术的不断发展,因特网成为主要的信息来源之一。在因特网广泛使用的同时,大量的色情图像也被非法的传播。虽然人们已经采取了一定措施对网络敏感图像进行过滤,但是这些过滤方法主要基于图像的文本信息或者IP地址,缺乏动态性和实时性。因此运用基于内容的敏感图像过滤技术成为一个迫切需要解决的课题。
传统的基于内容的图像检测算法主要根据图像中与肤色面积相关的一些特征进行分类,如肤色总面积比例、最大肤色连通域面积比例等。然而,这种方法虽然简单快捷,但是在保证较高正检率的前提下,其误检率往往较高,效果不甚理想。而本文针对以往算法单纯依据肤色特征分类的一些不足,提出了一种在人脸信息提取和人体躯干定位的基础上,动态地检测肤色,而后对敏感部位进行识别的图像过滤方法。同时,本文利用一个简单快速的决策树分类器,将传统算法与新提出的方法相结合,构建了一个多层次的敏感图像分类系统。实验结果表明,该敏感图像检测系统能够达到较高的正检率。并且和以往的方法相比,该系统又大大降低了误检率。经实验,在对266幅裸露较多的人体图像的检测中,新方法降低了近20%的误检率,达到了37.97%。同时在对193幅敏感人体图像的检测中,该方法提升了约15%的正检率,达到了80.31%。而在对1259幅非敏感图像的检测中,该方法总正检率达到了81.70%。
Building up content-based image filtering on the Internet is a crucial issue because of the recent explosion in the amount of online images .But the usual filtering methods are only based on the IP address and the web text content .And even if combined with a content-based image detection, the precision was always unsatisfactory when popularly employing the simple static skin-color detection algorithm. In this paper, a method for detecting skin color which is based on human face and body positional information have been proposed. And based on the body trunk search and skin-color detection, an erotogenic-part recognition algorithm is introduced. Eventually, a network pornography image filtering system is constructed .Concretely, the main contributes of the paper are:
1) We review the development of face detecting technology .In the view of veracity and velocity, we select Viola’s rapid face detecting algorithm which is based on the Integral Image and the AdaBoost training .The method make use of the Integral Image to calculate the rectangle features which will compose a strong classifier after the AdaBoost training .Because the rapid calculating of the integral image ,the simple-to-complex strategy and the cascade structure of the classifier ,the classifying speed increase greatly .We implement the face detecting with the Intel corporation’s OpenCV image processing library .In order to successfully detect rotated faces ,each input image is passed to face detector three times .The first time is the original image ,the next two times are its rotated variants with rotation angle of 45 degrees anticlockwise and clockwise respectively .On the base of rotated face detection we calculate the average and the variance of quantum in the YCrCb color space as the face skin features .Then we employ the thinking of ellipse fitting to establish a human body model with a series of mathematic formulas .As a result, we achieve to getting the body approximate location .Because there is large naked skin area in erotic image ,the body here means the largest part of exposed skin area in this algorithm.
2) During the summary of classic skin algorithm ,we analyze the character of the skin as well as the color space .Comparison has been made among several skin color models .The skin color models include Statistical Color Model; Gaussian Mixture Model; Chroma Space Model .The classic skin algorithm also use the texture detecting for the filtration .After the analysis and comparison of the texture usual algorithm ,our system introduce the one-rank-gray stat considering the result and speed .In the classic skin detection we only use the color information and ignore the importance of the pixel position information .To our common sense ,even the same color in different distribution maybe present different object .And the skin pixel appear more in the center area and around the object than other area .Aiming at the deficiency of the traditional skin color algorithm, we propose the dynamic threshold method based on the pixel positional information using the body approximate locating algorithm. Firstly we define relative distance to measure the pixel position .Here the relative distance means the minimum of the distance from the pixel to the center of the face ellipse and the body ellipse .Secondly we take advantage of the Bayes theory to establish the relation between the pixel distance and the skin probability .The skin probability of the pixel will be larger when its relative distance is more little .Then the skin threshold can be dynamically determined according to the skin probability and the face skin feature. And based on the approximate trunk location and dynamic skin-color detection, we ascertain a geometric region containing trunk by region growing and extracting region of interesting. And we adjust the trunk boundary more accurately in it by virtue of skin-color boundary.
3) The implement of erotogenic-part detector has achieved on the basis of trunk location. It is constituted by two sub detector: breast feature detector and pubes feature detector. In the breast feature recognition, we firstly transform and strengthen original image to make the breast feature more obvious. Then recognize the candidate feature to breast one by virtue of some attributes of the breast feature’s own, such as ellipse-like shape, the amount of edge pixels, the area of skin pixels surround it. And in the pubes recognition, several pubes filtering windows like“凹”are proposed which are similar to Haar-Like feature. And the whole image is scanned with these windows in different size and at different orientations to search and recognize the pubes feature according with the conditions given. These conditions is concerned with the ratio of the skin area, the dark area and the edge pixels’amount within the window,. And a rapid calculation of pixel summation within a arbitrary rectangle has achieved by virtue of two integral images,i.e. Summed Area Table (SAT) and Rotated Summed Area Table (RSAT) which are presented by Viola.
4) We construct a multilayer pornography image filtering system by integrating traditional method and our new approach. At first, we need to decide which kind of skin-color features should be chosen for classification according to the result of face detection. If a face is recognized, the new algorithm based on dynamic skin detection, trunk location and erotogenic-part recognition is used. Otherwise, we merely extract the skin features by employing traditional method. We chose 993 images of normally dressed person, 266 images of much bare whereas not erotic person and 193 pornographic images all crawled from Internet. Then the ability of our classifier is seriously tested, and a thorough comparison was conducted between the previous skin-color detection algorithm and our new approach. As shown, the new method diminishes the false positive rate by about 20% to 37.97%.And it raise the detection rate by about 15% to 80.31%. Its total detection rate is 81.70% in all 1259 benign images.
5) At last, we introduce the application of pornography image filtering system in the IE browser. First the event mechanism of IE browser and the function of its assistor object are explained. Then we introduce the approach to capturing specific event using ATL. Eventually, we present the framework of our filter plug-in and the work flow of main models.
Results on extensive experiments indicate that, comparing with many previous methods, this system efficiently improve the precision and moreover, decline the false positive rate dramatically. Especially, in the case of two kinds of images below: a person dressing less but not naked; and a certain bare part of the person occupies a large area of the image due to the visual angle whereas the image is still not pornographic (e.g. the feature of hand or back), our method is expected to be more effective.
传统的基于内容的图像检测算法主要根据图像中与肤色面积相关的一些特征进行分类,如肤色总面积比例、最大肤色连通域面积比例等。然而,这种方法虽然简单快捷,但是在保证较高正检率的前提下,其误检率往往较高,效果不甚理想。而本文针对以往算法单纯依据肤色特征分类的一些不足,提出了一种在人脸信息提取和人体躯干定位的基础上,动态地检测肤色,而后对敏感部位进行识别的图像过滤方法。同时,本文利用一个简单快速的决策树分类器,将传统算法与新提出的方法相结合,构建了一个多层次的敏感图像分类系统。实验结果表明,该敏感图像检测系统能够达到较高的正检率。并且和以往的方法相比,该系统又大大降低了误检率。经实验,在对266幅裸露较多的人体图像的检测中,新方法降低了近20%的误检率,达到了37.97%。同时在对193幅敏感人体图像的检测中,该方法提升了约15%的正检率,达到了80.31%。而在对1259幅非敏感图像的检测中,该方法总正检率达到了81.70%。
Building up content-based image filtering on the Internet is a crucial issue because of the recent explosion in the amount of online images .But the usual filtering methods are only based on the IP address and the web text content .And even if combined with a content-based image detection, the precision was always unsatisfactory when popularly employing the simple static skin-color detection algorithm. In this paper, a method for detecting skin color which is based on human face and body positional information have been proposed. And based on the body trunk search and skin-color detection, an erotogenic-part recognition algorithm is introduced. Eventually, a network pornography image filtering system is constructed .Concretely, the main contributes of the paper are:
1) We review the development of face detecting technology .In the view of veracity and velocity, we select Viola’s rapid face detecting algorithm which is based on the Integral Image and the AdaBoost training .The method make use of the Integral Image to calculate the rectangle features which will compose a strong classifier after the AdaBoost training .Because the rapid calculating of the integral image ,the simple-to-complex strategy and the cascade structure of the classifier ,the classifying speed increase greatly .We implement the face detecting with the Intel corporation’s OpenCV image processing library .In order to successfully detect rotated faces ,each input image is passed to face detector three times .The first time is the original image ,the next two times are its rotated variants with rotation angle of 45 degrees anticlockwise and clockwise respectively .On the base of rotated face detection we calculate the average and the variance of quantum in the YCrCb color space as the face skin features .Then we employ the thinking of ellipse fitting to establish a human body model with a series of mathematic formulas .As a result, we achieve to getting the body approximate location .Because there is large naked skin area in erotic image ,the body here means the largest part of exposed skin area in this algorithm.
2) During the summary of classic skin algorithm ,we analyze the character of the skin as well as the color space .Comparison has been made among several skin color models .The skin color models include Statistical Color Model; Gaussian Mixture Model; Chroma Space Model .The classic skin algorithm also use the texture detecting for the filtration .After the analysis and comparison of the texture usual algorithm ,our system introduce the one-rank-gray stat considering the result and speed .In the classic skin detection we only use the color information and ignore the importance of the pixel position information .To our common sense ,even the same color in different distribution maybe present different object .And the skin pixel appear more in the center area and around the object than other area .Aiming at the deficiency of the traditional skin color algorithm, we propose the dynamic threshold method based on the pixel positional information using the body approximate locating algorithm. Firstly we define relative distance to measure the pixel position .Here the relative distance means the minimum of the distance from the pixel to the center of the face ellipse and the body ellipse .Secondly we take advantage of the Bayes theory to establish the relation between the pixel distance and the skin probability .The skin probability of the pixel will be larger when its relative distance is more little .Then the skin threshold can be dynamically determined according to the skin probability and the face skin feature. And based on the approximate trunk location and dynamic skin-color detection, we ascertain a geometric region containing trunk by region growing and extracting region of interesting. And we adjust the trunk boundary more accurately in it by virtue of skin-color boundary.
3) The implement of erotogenic-part detector has achieved on the basis of trunk location. It is constituted by two sub detector: breast feature detector and pubes feature detector. In the breast feature recognition, we firstly transform and strengthen original image to make the breast feature more obvious. Then recognize the candidate feature to breast one by virtue of some attributes of the breast feature’s own, such as ellipse-like shape, the amount of edge pixels, the area of skin pixels surround it. And in the pubes recognition, several pubes filtering windows like“凹”are proposed which are similar to Haar-Like feature. And the whole image is scanned with these windows in different size and at different orientations to search and recognize the pubes feature according with the conditions given. These conditions is concerned with the ratio of the skin area, the dark area and the edge pixels’amount within the window,. And a rapid calculation of pixel summation within a arbitrary rectangle has achieved by virtue of two integral images,i.e. Summed Area Table (SAT) and Rotated Summed Area Table (RSAT) which are presented by Viola.
4) We construct a multilayer pornography image filtering system by integrating traditional method and our new approach. At first, we need to decide which kind of skin-color features should be chosen for classification according to the result of face detection. If a face is recognized, the new algorithm based on dynamic skin detection, trunk location and erotogenic-part recognition is used. Otherwise, we merely extract the skin features by employing traditional method. We chose 993 images of normally dressed person, 266 images of much bare whereas not erotic person and 193 pornographic images all crawled from Internet. Then the ability of our classifier is seriously tested, and a thorough comparison was conducted between the previous skin-color detection algorithm and our new approach. As shown, the new method diminishes the false positive rate by about 20% to 37.97%.And it raise the detection rate by about 15% to 80.31%. Its total detection rate is 81.70% in all 1259 benign images.
5) At last, we introduce the application of pornography image filtering system in the IE browser. First the event mechanism of IE browser and the function of its assistor object are explained. Then we introduce the approach to capturing specific event using ATL. Eventually, we present the framework of our filter plug-in and the work flow of main models.
Results on extensive experiments indicate that, comparing with many previous methods, this system efficiently improve the precision and moreover, decline the false positive rate dramatically. Especially, in the case of two kinds of images below: a person dressing less but not naked; and a certain bare part of the person occupies a large area of the image due to the visual angle whereas the image is still not pornographic (e.g. the feature of hand or back), our method is expected to be more effective.
引文
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[2] 中国互联网络信息中心.“第十五次中国互联网络发展状况统计报告”[EB/OL].URL(http://www.cnnic.net.cn/),2004.3.
[3] 北京展翅鸟科技公司,网络爸爸(www.tueagles.com/baba)(http://www.tueagles.com/baba/softnews/1020.htm).2003.10.
[4] 美萍电脑软件工作室,美萍反黄专家.URL(http://www.mpsoft.net/ shield.Htm),2005.3.
[5] 飞涛软件工作室,护花使者. URL(http://www.18ie.com/),2005.3.
[6] D.A.Forsyth, M. Fleck, and C. Bregler. Finding naked people. In Proc. Fourth European Conference on Computer Vision, 1996:593-602.
[7] D.A.Forsyth,Identifying nude pictures,IEEE, 1996.
[8] James Ze Wang, System for screening objectionable images using daubechies' wavelets and color histograms.In Proc.IDMJ, 1997.
[9] Michael J. Jones and James M. Rehg. Statistical Color Model with Application to Skin Detection. Cambridge Research laboratory Technical Report Series, CRL 98/11, December 1998.
[10] Alexandru F. Drimbarean, Image processing techniques to detect and filter objectionable images based on skin tone and shape recognition, IEEE, 2001.
[11] 段立娟,崔国勤,高文,张洪明. 多层次特定类型图像过滤方法[J]. 计算机辅助设计与图形学学报,2002,14(5):404-409.
[12] Samaria FS. Face Recognition Using Hidden Markov Models. PhD thesis, Univ. of Cambridge, 1994.
[13] Belhumeur P, Hespanha J, Kriegman D. Eigenfaces vs. Fisher faces: Recognition Using Class Specific Linear Projection. IEEE Trans. Pattern Analysis and Machine Intelligence, vol.19, no. 7, pp.711~720, 1997.
[14] Lu CY. Research on some issues of face recognition and system implementation [PhD dissertation]. Tsinghua University, Beijing, 1998(in Chinese with English abstract).
[15] Rowley H A, Baluja S, Kanade T. Neural network-based face detection. IEEE Trans. On PAMI, 1998, 20(1): 23~38.
[16] 梁路宏,人脸检测与跟踪研究,清华大学计算机系博士论文,2001:全部.
[17] Viola Paul, Jones Michael. Rapid Object Detection using a Boosted Cascade of Simple Features. In: Proc. IEEE Conf. on Computer Vision and Pattern Recognition, Hawaii, USA, 2001.
[18] Freund, Y. and Schapire, R. E. (1997). “A decision theoretic generalization of on-line learning and an application to boosting”, Journal of Computer and System Science, 55(1): pages: 119–139.
[19] R. Schapire. “A brief introduction to boosting”. In Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence, 1999.
[20] R. Schapire, Y. Freund, P. Bartlett, and W. Lee. “Boosting the margin: a new explanation for the effectiveness of voting methods.” In Proc. 14th International Conference on Machine learning, pages: 322-330. Morgan Kaufmann, 1977.
[21] R. E. Schapire and Y. Singer. “Improved boosting algorithms using confidence-rated predictions”. In Proceedings of the Eleventh Annual Conference on Computational Learning Theory, pages: 80-91, 1998.
[22] Y. Freund and R. E. Schapire. Experiments with a New Boosting Algorithm.In Proceedings of the 13th International Conference on Machine Learning,pp.148-156,Morgan Kaufmann, 1996.
[23] Intel Cooperation. OpenCV 参考手册,2001:全部.
[24] Qing Fang Zheng,Ming Ji Zhang,Wei Qiang Wang.“A Hybrid Approach to Dectect Adult Web Images” in 5th Pacific Rim Conference on Tokyo, Japan, 2004.
[25] 吴相豪, 申铉京. 基于像素的三种肤色检测模型的比较与研究[J].计算机应用研究,2003.09 精扩本:430-432.
[26] 杨金锋,傅周宇,谭铁牛,胡卫明.一种新型的基于内容的图像识别与过滤方法.通信学报, 2004-7, 25(7):93-104.
[27] 胡冠宇. 基于肤色之裸体影像侦测之研究,台湾国立成功大学,硕士论文,2004:全部.
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