基于肤色分割和PCA的支持向量新颖检测的人脸检测研究
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摘要
近年来,基于生物特征的身份认证技术得到了飞速发展,人脸检测作为生物特征认证技术的一个重要应用,越来越受到人们的重视。人脸检测在视觉图像处理、人机交互、表情识别、视频监控以及基于内容检索等领域有着很重要的作用和实际应用价值。当前,已有许多关于人脸检测算法的研究,但是由于人脸本身具有相当的复杂性,很难找到一种算法能够适应各种条件下的检测任务,更多的研究是基于多种算法的有效结合,从而提高算法的综合检测性能。本文对当前主要的人脸检测算法的优缺点进行了简述和分析,实现了肤色检测与级联支持向量新颖检测(SVND)结合的人脸检测方法。
首先对基于肤色的人脸检测算法进行了研究。通过对肤色在不同彩色空间中的聚类性分析,选择在H-CbCr彩色分量上进行简单肤色模型的分割,然后在YCbCr彩色空间中建立高斯模型对简单肤色模型的分割区域进行二次分割,并在肤色分割的过程中加入边缘检测,从而将大片连通肤色或类肤色区域进行分离。在对图像进行二值化时,选择改进的最大类间方差(Otsu)阈值分割法,通过滤波、形态学处理,以消除噪声的影响,最终较好地分割出人脸候选区域。
然后对基于SVND的人脸检测方法进行了研究和分析。详细介绍了二分类SVM的检测原理、1类SVM检测方法,并实现了级联SVND的人脸检测算法。将样本图像的像素作为特征值进行训练和测试,会导致样本维数过高而增加计算的复杂度,针对这一问题,本文先对样本图像进行主成分分析(PCA)。通过在MIT人脸数据库及真实图像上对级联SVND使用不同特征(像素特征和PCA特征)情况下的分类性能进行了比较,验证了PCA降维处理在人脸检测中的有效性。
最后针对肤色检测和基于PCA特征的级联SVND在人脸检测中各自的特点,本文将这两种方法结合起来进行人脸检测,实验验证了本文采用的方法提高了人脸检测的检测率并在一定程度上降低了误检率和检测时间。为了充分利用肤色检测的结果,对级联SVND的检测窗口大小进行调整,进一步减少了检测时间。
Recently, the identity authentication technology based on biometric has been developed rapidly. Face detection as an important application of biometric authentication technology has been paid more and more attentions. Face detection plays a very important role and practical value in the visual image processing, human-computer interaction, facial expression recognition, video surveillance, content-based search, etc. Many face detection algorithms have been proposed. Because of the complexity of the face, it is difficult to find an algorithm, which can adapt to all kinds of conditions of detection task. More research is based on a variety of effective combination of the algorithms to improve the detection performance of the algorithms. In this paper, the description and analysis about the advantages and disadvantages in current main face detection algorithms has been given. A face detection method combined with the skin color detection and the novel cascade support vector (SVND) is implemented.
First, face detection algorithm based on skin color is studied. Through the analyis about clustering of skin color in different color space, we select the H-CbCr color components of the color model for simple division. Then the Gaussian model is set up in the YCbCr color space of simple skin color model segmentation area for secondary division, and the edge detection is added in the process of color segmentation, thus large areas of communicating color or skin color is separated. During the binarization of image, choose the improved between-cluster variance (Otsu) threshold segmentation method for binary, by filtering, morphological processing, in order to eliminate the influence of noise. Face candidate region is better splited up at last.
Then, the face method based on SVND is studied and analyzed. Principle of the two classification support vector machine (SVM) method and one-class SVM detection method are introduced in details. The cascade SVND face detection algorithm is realized. The pixel values of the sample image for training and testing will lead to the increasing of the dimension of the sample and the complexity of the calculation. In order to solve this problem, the principal component analysis (PCA) is applied to sample image firstly in this article. Based on the MIT face database and real images, for a cascade SVND, we give the performance comparisons for different characteristic (Pixel features and PCA features) and verify the PCA dimensionality reduction in the effectiveness of face detection.
Finally, for the characteristics of cascade SVND based PCA feature and skin detection in face detection, this paper combines these two methods for face detection. Improvement in the detection rate of face detection and reduction in the false detection rate and detection time are verified through experiments. In order to make full use of the skin color segmentation results, the size of cascade SVND detection window is adjusted and the detection time is reduced further.
首先对基于肤色的人脸检测算法进行了研究。通过对肤色在不同彩色空间中的聚类性分析,选择在H-CbCr彩色分量上进行简单肤色模型的分割,然后在YCbCr彩色空间中建立高斯模型对简单肤色模型的分割区域进行二次分割,并在肤色分割的过程中加入边缘检测,从而将大片连通肤色或类肤色区域进行分离。在对图像进行二值化时,选择改进的最大类间方差(Otsu)阈值分割法,通过滤波、形态学处理,以消除噪声的影响,最终较好地分割出人脸候选区域。
然后对基于SVND的人脸检测方法进行了研究和分析。详细介绍了二分类SVM的检测原理、1类SVM检测方法,并实现了级联SVND的人脸检测算法。将样本图像的像素作为特征值进行训练和测试,会导致样本维数过高而增加计算的复杂度,针对这一问题,本文先对样本图像进行主成分分析(PCA)。通过在MIT人脸数据库及真实图像上对级联SVND使用不同特征(像素特征和PCA特征)情况下的分类性能进行了比较,验证了PCA降维处理在人脸检测中的有效性。
最后针对肤色检测和基于PCA特征的级联SVND在人脸检测中各自的特点,本文将这两种方法结合起来进行人脸检测,实验验证了本文采用的方法提高了人脸检测的检测率并在一定程度上降低了误检率和检测时间。为了充分利用肤色检测的结果,对级联SVND的检测窗口大小进行调整,进一步减少了检测时间。
Recently, the identity authentication technology based on biometric has been developed rapidly. Face detection as an important application of biometric authentication technology has been paid more and more attentions. Face detection plays a very important role and practical value in the visual image processing, human-computer interaction, facial expression recognition, video surveillance, content-based search, etc. Many face detection algorithms have been proposed. Because of the complexity of the face, it is difficult to find an algorithm, which can adapt to all kinds of conditions of detection task. More research is based on a variety of effective combination of the algorithms to improve the detection performance of the algorithms. In this paper, the description and analysis about the advantages and disadvantages in current main face detection algorithms has been given. A face detection method combined with the skin color detection and the novel cascade support vector (SVND) is implemented.
First, face detection algorithm based on skin color is studied. Through the analyis about clustering of skin color in different color space, we select the H-CbCr color components of the color model for simple division. Then the Gaussian model is set up in the YCbCr color space of simple skin color model segmentation area for secondary division, and the edge detection is added in the process of color segmentation, thus large areas of communicating color or skin color is separated. During the binarization of image, choose the improved between-cluster variance (Otsu) threshold segmentation method for binary, by filtering, morphological processing, in order to eliminate the influence of noise. Face candidate region is better splited up at last.
Then, the face method based on SVND is studied and analyzed. Principle of the two classification support vector machine (SVM) method and one-class SVM detection method are introduced in details. The cascade SVND face detection algorithm is realized. The pixel values of the sample image for training and testing will lead to the increasing of the dimension of the sample and the complexity of the calculation. In order to solve this problem, the principal component analysis (PCA) is applied to sample image firstly in this article. Based on the MIT face database and real images, for a cascade SVND, we give the performance comparisons for different characteristic (Pixel features and PCA features) and verify the PCA dimensionality reduction in the effectiveness of face detection.
Finally, for the characteristics of cascade SVND based PCA feature and skin detection in face detection, this paper combines these two methods for face detection. Improvement in the detection rate of face detection and reduction in the false detection rate and detection time are verified through experiments. In order to make full use of the skin color segmentation results, the size of cascade SVND detection window is adjusted and the detection time is reduced further.
引文
[1]Cha Zhang and Zhengyou Zhang. A Surver of Recent Advance in Face Detection[J]. Technical Report, Microsoft Research MSR-TR-2010-66,2010.
[2]C. JayaMohan, M. Saravana Deepak, M. L. Alphin Ezhil Manuel, and D. C Joy Winnie Wise. Face detection and recognition system in bank robbery using CFV modules based on video recordings[C]. Computer Science and Engineering,2013:13255-13258.
[3]汤秋艳.基于肤色分割的彩色图像检测及特征定位[D].沈阳理工大学硕士学位论文,2007.
[4]刘铮.变化光照条件下人脸检测技术研究[D].国防科技大学硕士学位论文,2006.
[5]柯研.基于肤色模型的人脸检测算法研究[D].华中科技大学硕士学位论文,2008.
[6]王一伟.基于肤色和AdaBoost算法的多角度多姿态人脸检测[D].北京交通大学硕士学位论文,2011.
[7]Pabbaraju A, Puchakayala S. Face Recognition in Mobile Devices[J]. Electrical Engineering and Computer Science, University of Michigan,2010.
[8]冷严,王汇源,王增峰.基于平均模板的人脸图像纠正[J].计算机应用,2005,8：1774-1776.
[9]马桂英.基于眼睛特征定位的人脸模板匹配算法研究[J].科技资讯,2009,31：15.
[10]Yuille A. Hallinan P, Cohen D. Feature Extrantion from Faces Using Deformable Templates[J]. Internationl Journal of Computer Vision,1992,8(2):99-111.
[11]Jiangguo Wang, Tieniu Tan. A new face detection method based on shape information[J]. Pattern Recognition letters,2000,21(6):463-471.
[12]山世光,高文,陈熙林.基于纹理分布和变形模板对面部特征提取[J].软件学报,2001,12(4)：570-577.
[13]Yang G, Huang T S. Human face detection in a complex background[J]. Pattern recognition,1994,27(1):53-63.
[14]肖红,南威治.基于肤色模型与人脸结构特征的人脸检测[J].科学技术与工程,2010,21(10)：5179-5183.
[15]淘劲草.人脸检测与识别的算法研究[D].电子科技大学硕士学位论文,2009.
[16]宋红,石峰.面向视频监控的快速人脸检测与分析[J].兵工学报2006,27(2)：252-257.
[17]齐永锋,张家树,火元莲.一种基于肤色与改进的LBP的人脸检测方法[J].光电子.激光,2009,6(20)：195-201.
[18]Xu Qingshi, Yue Xiang. Method of face detection based on impoved YUV color space[J]. Computer Engineering and Applications,2008,44(34):158-162.
[19]Turk M, Pentland A. Eigenfaces for recognition[J]. Journal of cognitive neuroscience, 1991,3(1):71-86.
[20]Rowley H A, Baluja S, Kanade T. Neural network-based face detection[J]. Pattern Analysis and Machine Intelligence, IEEE Transactions on,1998,20(1):23-38.
[21]Viola P, Jones M. Robust Real-time Object Detection[R]. Technical Repport 2001/01, Compaq CRL,2001-02.
[22]魏东生,李林青Adaboost人脸检测方法的改进[J].计算机应用,2006,2(3)：619-621.
[23]徐淑峰,孙炜,赵峰Adaboost人脸检测算法在嵌入式平台上的优化及实现[J].信息技术,2008,32(6)：149-151.
[24]邓乃扬,田英杰.支持向量机—理论、算法与扩展[M].北京：科学出版社,2009.
[25]Osuna E, Freund R, Girosi F. Training support vector machines:An application to face detection [A]. IEEE,1997:130-136.
[26]Hongliang Jin, Qingshan Liu, Hanqing Lu, Xiaofeng Tong. Face detection using one-class SVM in color image[J]. IEEE,2004:1431-1434.
[27]Aiping C, Lian P, Yaobin T, etal. Face detection technology based on skin color segmentation and template matching[C]. Education Technology and Computer Science (ETCS),2010,2:708-711.
[28]姜丽,许杰,田丽,孟艳君.基于YCbCr空间的自适应肤色分割方法[J].信息科技,2010.7：235-236.
[29]Kekre H B, Thepade S D, Lodha V, etal. Augmentation of Block Truncation Coding based Image Retrieval by using Even and Odd Images with Sundry Color Space[J]. Int. Journal on Computer Science and Engg,2010,2(08):2535-2544.
[30]Kekre H B, Thepade S D, Chaturvedi R N, etal. Walsh, Sine, Haar& Cosine Transform With Various Color Spaces for 'Color to Gray and Back'[J]. International Journal of Image Processing (IJIP),2012,6(5):349.
[31]张洪明,赵德斌.高文.基于肤色模型、神经网络和人脸结构模型的平面旋转人脸检测[J].计算机学报,2002：25(11)：1250-1256.
[32]武秀.基于肤色分割和统计学的彩色人脸检测实现[D].西南交通大学硕士学位论文,2008.
[33]Belhumeur P N, Jacobs D W, Kriegman D J, etal. Localizing parts of faces using a consensus of exemplars[C]. Computer Vision and Pattern Recognition (CVPR),2011 IEEE Conference on. IEEE,2011:545-552.
[34]Basilio J A M, Torres G A, Perez G S, etal. Explicit image detection using YCbCr space color model as skin detection[C]. Proc. Of the 2011 American conference on applied mathematics and the 5th WSEAS international conference on Computer engineering and applications,2011:123-128.
[35]BerbarM A. Face and facial features detection in color images[J]. Coputer Engineering andSciences Department, Gatar University,2006,541(803):177-182.
[36]王传旭,李雪.基于高斯模型和支持向量机的人脸检测方法[J].微计算机信息,2009：225-227.
[37]沈常雨.肤色建模和肤色分割的人脸定位研究[J].光电工程,2007,34(9)：103-107.
[38]Mohammed K, Ennehar B C, Yamina T. Skin detection using gaussian mixture models in YCbCr and HSV color space [J]. AWERProcedia Information Technology and Computer Science,2012,1.
[39]Kherchaoui S, Houacine A. Face detection based on a model of the skin color with constraints and template matching[C]. Machine and Web Intelligence (ICMWI),2010 International Conference on. IEEE,2010:469-472.
[40]Yoon H, Han Y, Hahn H. Image contrast Enhancement based Sub-histogram Equalization Technique without over-equalization Noise[J]. World Academy of Science, Engineering and Technology,2009,50:2009.
[41]Inalou S A, Kasaei S. Adaboost-based face detection in color images with low false alarm[C]. Computer Modeling and Simulation,2010. ICCMS'10. Second International Conference on. IEEE,2010,2:107-111.
[42]胡晓燕,张宇.基于肤色的人脸检测算法研究[J].合肥工业大学学报,2012,35(7)：908-912.
[43]赵宏.人脸表情识别算法研究[D].华东理工大学硕士学位论文,2011.
[44]冯亚丽,蒋文文,刘泽光.一种改进的基于肤色分割和PCA人脸检测方法[J].科学技术与工程,2010,10(10)：2344-2348.
[45]黄玉程,胡国清,吴雄英等.人脸图像边缘检测的方法研究和应用[J].计算机工程,2006,32(18)：220-224.
[46]顾佳玲,彭宏京.基于Canny算子的肤色分割在彩色图像中的人脸检测[J].计算机工程与应用,2007,43(22)：220-222.
[47]Berbar M A. Face and facial features detection in color images [J]. Computer Engineering and Science Department, Gatar University,2006,541(803):177-182.
[48]唐权华,周艳等.中值滤波算法的研究与改进[J].微计算机信息,2008,8-25：186-188.
[49]张铮,王艳平,薛桂香.数字图像处理与机器视觉[M].北京：人民邮电出版社,2010.
[50]申玉玉.彩色图像中的人脸检测[D].南京理工大学硕士学位论文,2010.
[51]宋召青,崔和,胡云安.支持向量机理论的研究与进展[J].海军航空工程学院学报, 2008,02(26)：143-152.
[52]Balabin R M, Lomakina E I. Support vector machine regression (SVR/LS-SVM)-an alternative to neural networks (ANN) for analytical chemistry? Comparison of nonlinear methods on near infrared (NIR) spectroscopy data[J]. Analyst,2011,136(8):1703-1712.
[53]Chen H L, Yang B, Liu J, etal. A support vector machine classifier with rough set-based feature selection for breast cancer diagnosis [J]. Expert Systems with Applications,2011, 38(7):9014-9022.
[54]Orru G, Pettersson-Yeo W, Marquand A F, etal. Using support vector machine to identify imaging biomarkers of neurological and psychiatric disease:a critical review[J]. Neuroscience & Biobehavioral Reviews,2012,36(4):1140-1152.
[55]Geebelen D, Suykens J A K, Vandewalle J. Reducing the number of support vectors of SVM classifiers using the smoothed separable case approximation[J]. Neural Networks and Learning Systems, IEEE Transactions on,2012,23(4):682-688.
[56]Bernhard Scholkopf, Robert Williamson, Alex Smola, etal. Support vector method for novelty detection[J]. Advances in neural information processing systems,2000,12(3): 582-588.
[57]Bernhard Scholkopf, John C. Platt, John Shawe-Taylor, etal. Estimating the support of a high-dimensional distribution[J]. Neural computation,1999:1443-1471.
[58]Senf A, Chen X, Zhang A. Comparison of one-class SVM and two-class SVM for fold recognition[C]. Neural Information Processing. Springer Berlin Heidelberg,2006: 140-149.
[59]Zhang L, Weida Z, Lin Y, etal. Support vector novelty detection with dot product kernels for non-spherical data[C]. Information and Automation,2008. ICIA 2008. International Conference on. IEEE,2008:41-46.
[60]Hao P Y. Fuzzy one-class support vector machines [J]. Fuzzy Sets and Systems,2008, 159(18):2317-2336.
[61]Pan Y, Chen J, Guo L. Robust bearing performance degradation assessment method based on improved wavelet packet-support vector data description[J]. Mechanical Systems and Signal Processing,2009,23(3):669-681.
[62]Sanchez-Hernandez C, Boyd D S, Foody G M. One-class classification for mapping a specific land-cover class:SVDD classification of fenland[J]. Geoscience and Remote Sensing, IEEE Transactions on,2007,45(4):1061-1073.
[63]R.O.Duda, P. E. Hart and D. G.Stork. Pattern Classification[M].2rd Edition, John Wiley&Sons,2000.
[64]胡志.基于支持向量新颖检测的人脸检测[D].西安电子科技大学硕士学位论文,2010.
[65]毕晓东.基于1类支持向量机的入侵检测模型研究[J].山东师范大学学报(自然科学版),2006,21(4)：35-37.
[66]陈怡安Hilbert空间下的再生核理论研究[D].东北师范大学硕士学位论文,2010.
[67]Wu M, Ye J. A small sphere and large margin approach for novelty detection using training data with outliers [J]. Pattern Analysis and Machine Intelligence, IEEE Transactions on,2009,31(11):2088-2092.
[68]郑小霞,钱峰.高斯核支持向量机分类和模型参数选择研究[J].计算机工程与应用,2006,02：77-79.
[69]Guerrero-Curieses A, Conde-Pardo P, Landesa-Vazquez I, etal. On the performance of kernel methods for skin color segmentation [J]. EURASIP Journal on Advances in Signal Processing,2009.
[70]曾阳艳.基于PCA方法的人脸特征提取和检测[J].人工智能及识别技术,2008(1)：743-744.
[71]Hamdy A.Drowsy Driver Assistant System:Principal Component Analysis[Z]. GUC, Cairo,2007.
[72]孙涛,谷士文,费耀平.基于PCA算法的人脸识别方法研究比较[J].现代电子技术,2007(1)：111-114.
[73]李雪.利用PCA进行快速人脸检测[J].电子设计应用,2009(2)：56-58.
[74]肖政宏.基于PCA和GMM的图像分类算法[J].计算机工程与设计,2007,11(27)：1951-1952.
[75]李磊.基于PCA降维的SVM人脸快速检测方法[J].青岛科技大学学报(自然科学版),2010,31(5)：528-532.
[76]El-Bakry H M. New fast principal component analysis for real-time face detection[J]. Machine Graphics & Vision International Journal,2009,18(4):405-425.
[77]孙崇璇.基于PCA主成分分析的人脸检测实现与分析[J].云南民族大学学报(自然科学版),2010,19(6)：439-443.
[78]霍中华.基于肤色和Adaboost算法的人脸检测方法研究[D].华南理工大学硕士学位论文,2012.
[2]C. JayaMohan, M. Saravana Deepak, M. L. Alphin Ezhil Manuel, and D. C Joy Winnie Wise. Face detection and recognition system in bank robbery using CFV modules based on video recordings[C]. Computer Science and Engineering,2013:13255-13258.
[3]汤秋艳.基于肤色分割的彩色图像检测及特征定位[D].沈阳理工大学硕士学位论文,2007.
[4]刘铮.变化光照条件下人脸检测技术研究[D].国防科技大学硕士学位论文,2006.
[5]柯研.基于肤色模型的人脸检测算法研究[D].华中科技大学硕士学位论文,2008.
[6]王一伟.基于肤色和AdaBoost算法的多角度多姿态人脸检测[D].北京交通大学硕士学位论文,2011.
[7]Pabbaraju A, Puchakayala S. Face Recognition in Mobile Devices[J]. Electrical Engineering and Computer Science, University of Michigan,2010.
[8]冷严,王汇源,王增峰.基于平均模板的人脸图像纠正[J].计算机应用,2005,8：1774-1776.
[9]马桂英.基于眼睛特征定位的人脸模板匹配算法研究[J].科技资讯,2009,31：15.
[10]Yuille A. Hallinan P, Cohen D. Feature Extrantion from Faces Using Deformable Templates[J]. Internationl Journal of Computer Vision,1992,8(2):99-111.
[11]Jiangguo Wang, Tieniu Tan. A new face detection method based on shape information[J]. Pattern Recognition letters,2000,21(6):463-471.
[12]山世光,高文,陈熙林.基于纹理分布和变形模板对面部特征提取[J].软件学报,2001,12(4)：570-577.
[13]Yang G, Huang T S. Human face detection in a complex background[J]. Pattern recognition,1994,27(1):53-63.
[14]肖红,南威治.基于肤色模型与人脸结构特征的人脸检测[J].科学技术与工程,2010,21(10)：5179-5183.
[15]淘劲草.人脸检测与识别的算法研究[D].电子科技大学硕士学位论文,2009.
[16]宋红,石峰.面向视频监控的快速人脸检测与分析[J].兵工学报2006,27(2)：252-257.
[17]齐永锋,张家树,火元莲.一种基于肤色与改进的LBP的人脸检测方法[J].光电子.激光,2009,6(20)：195-201.
[18]Xu Qingshi, Yue Xiang. Method of face detection based on impoved YUV color space[J]. Computer Engineering and Applications,2008,44(34):158-162.
[19]Turk M, Pentland A. Eigenfaces for recognition[J]. Journal of cognitive neuroscience, 1991,3(1):71-86.
[20]Rowley H A, Baluja S, Kanade T. Neural network-based face detection[J]. Pattern Analysis and Machine Intelligence, IEEE Transactions on,1998,20(1):23-38.
[21]Viola P, Jones M. Robust Real-time Object Detection[R]. Technical Repport 2001/01, Compaq CRL,2001-02.
[22]魏东生,李林青Adaboost人脸检测方法的改进[J].计算机应用,2006,2(3)：619-621.
[23]徐淑峰,孙炜,赵峰Adaboost人脸检测算法在嵌入式平台上的优化及实现[J].信息技术,2008,32(6)：149-151.
[24]邓乃扬,田英杰.支持向量机—理论、算法与扩展[M].北京：科学出版社,2009.
[25]Osuna E, Freund R, Girosi F. Training support vector machines:An application to face detection [A]. IEEE,1997:130-136.
[26]Hongliang Jin, Qingshan Liu, Hanqing Lu, Xiaofeng Tong. Face detection using one-class SVM in color image[J]. IEEE,2004:1431-1434.
[27]Aiping C, Lian P, Yaobin T, etal. Face detection technology based on skin color segmentation and template matching[C]. Education Technology and Computer Science (ETCS),2010,2:708-711.
[28]姜丽,许杰,田丽,孟艳君.基于YCbCr空间的自适应肤色分割方法[J].信息科技,2010.7：235-236.
[29]Kekre H B, Thepade S D, Lodha V, etal. Augmentation of Block Truncation Coding based Image Retrieval by using Even and Odd Images with Sundry Color Space[J]. Int. Journal on Computer Science and Engg,2010,2(08):2535-2544.
[30]Kekre H B, Thepade S D, Chaturvedi R N, etal. Walsh, Sine, Haar& Cosine Transform With Various Color Spaces for 'Color to Gray and Back'[J]. International Journal of Image Processing (IJIP),2012,6(5):349.
[31]张洪明,赵德斌.高文.基于肤色模型、神经网络和人脸结构模型的平面旋转人脸检测[J].计算机学报,2002：25(11)：1250-1256.
[32]武秀.基于肤色分割和统计学的彩色人脸检测实现[D].西南交通大学硕士学位论文,2008.
[33]Belhumeur P N, Jacobs D W, Kriegman D J, etal. Localizing parts of faces using a consensus of exemplars[C]. Computer Vision and Pattern Recognition (CVPR),2011 IEEE Conference on. IEEE,2011:545-552.
[34]Basilio J A M, Torres G A, Perez G S, etal. Explicit image detection using YCbCr space color model as skin detection[C]. Proc. Of the 2011 American conference on applied mathematics and the 5th WSEAS international conference on Computer engineering and applications,2011:123-128.
[35]BerbarM A. Face and facial features detection in color images[J]. Coputer Engineering andSciences Department, Gatar University,2006,541(803):177-182.
[36]王传旭,李雪.基于高斯模型和支持向量机的人脸检测方法[J].微计算机信息,2009：225-227.
[37]沈常雨.肤色建模和肤色分割的人脸定位研究[J].光电工程,2007,34(9)：103-107.
[38]Mohammed K, Ennehar B C, Yamina T. Skin detection using gaussian mixture models in YCbCr and HSV color space [J]. AWERProcedia Information Technology and Computer Science,2012,1.
[39]Kherchaoui S, Houacine A. Face detection based on a model of the skin color with constraints and template matching[C]. Machine and Web Intelligence (ICMWI),2010 International Conference on. IEEE,2010:469-472.
[40]Yoon H, Han Y, Hahn H. Image contrast Enhancement based Sub-histogram Equalization Technique without over-equalization Noise[J]. World Academy of Science, Engineering and Technology,2009,50:2009.
[41]Inalou S A, Kasaei S. Adaboost-based face detection in color images with low false alarm[C]. Computer Modeling and Simulation,2010. ICCMS'10. Second International Conference on. IEEE,2010,2:107-111.
[42]胡晓燕,张宇.基于肤色的人脸检测算法研究[J].合肥工业大学学报,2012,35(7)：908-912.
[43]赵宏.人脸表情识别算法研究[D].华东理工大学硕士学位论文,2011.
[44]冯亚丽,蒋文文,刘泽光.一种改进的基于肤色分割和PCA人脸检测方法[J].科学技术与工程,2010,10(10)：2344-2348.
[45]黄玉程,胡国清,吴雄英等.人脸图像边缘检测的方法研究和应用[J].计算机工程,2006,32(18)：220-224.
[46]顾佳玲,彭宏京.基于Canny算子的肤色分割在彩色图像中的人脸检测[J].计算机工程与应用,2007,43(22)：220-222.
[47]Berbar M A. Face and facial features detection in color images [J]. Computer Engineering and Science Department, Gatar University,2006,541(803):177-182.
[48]唐权华,周艳等.中值滤波算法的研究与改进[J].微计算机信息,2008,8-25：186-188.
[49]张铮,王艳平,薛桂香.数字图像处理与机器视觉[M].北京：人民邮电出版社,2010.
[50]申玉玉.彩色图像中的人脸检测[D].南京理工大学硕士学位论文,2010.
[51]宋召青,崔和,胡云安.支持向量机理论的研究与进展[J].海军航空工程学院学报, 2008,02(26)：143-152.
[52]Balabin R M, Lomakina E I. Support vector machine regression (SVR/LS-SVM)-an alternative to neural networks (ANN) for analytical chemistry? Comparison of nonlinear methods on near infrared (NIR) spectroscopy data[J]. Analyst,2011,136(8):1703-1712.
[53]Chen H L, Yang B, Liu J, etal. A support vector machine classifier with rough set-based feature selection for breast cancer diagnosis [J]. Expert Systems with Applications,2011, 38(7):9014-9022.
[54]Orru G, Pettersson-Yeo W, Marquand A F, etal. Using support vector machine to identify imaging biomarkers of neurological and psychiatric disease:a critical review[J]. Neuroscience & Biobehavioral Reviews,2012,36(4):1140-1152.
[55]Geebelen D, Suykens J A K, Vandewalle J. Reducing the number of support vectors of SVM classifiers using the smoothed separable case approximation[J]. Neural Networks and Learning Systems, IEEE Transactions on,2012,23(4):682-688.
[56]Bernhard Scholkopf, Robert Williamson, Alex Smola, etal. Support vector method for novelty detection[J]. Advances in neural information processing systems,2000,12(3): 582-588.
[57]Bernhard Scholkopf, John C. Platt, John Shawe-Taylor, etal. Estimating the support of a high-dimensional distribution[J]. Neural computation,1999:1443-1471.
[58]Senf A, Chen X, Zhang A. Comparison of one-class SVM and two-class SVM for fold recognition[C]. Neural Information Processing. Springer Berlin Heidelberg,2006: 140-149.
[59]Zhang L, Weida Z, Lin Y, etal. Support vector novelty detection with dot product kernels for non-spherical data[C]. Information and Automation,2008. ICIA 2008. International Conference on. IEEE,2008:41-46.
[60]Hao P Y. Fuzzy one-class support vector machines [J]. Fuzzy Sets and Systems,2008, 159(18):2317-2336.
[61]Pan Y, Chen J, Guo L. Robust bearing performance degradation assessment method based on improved wavelet packet-support vector data description[J]. Mechanical Systems and Signal Processing,2009,23(3):669-681.
[62]Sanchez-Hernandez C, Boyd D S, Foody G M. One-class classification for mapping a specific land-cover class:SVDD classification of fenland[J]. Geoscience and Remote Sensing, IEEE Transactions on,2007,45(4):1061-1073.
[63]R.O.Duda, P. E. Hart and D. G.Stork. Pattern Classification[M].2rd Edition, John Wiley&Sons,2000.
[64]胡志.基于支持向量新颖检测的人脸检测[D].西安电子科技大学硕士学位论文,2010.
[65]毕晓东.基于1类支持向量机的入侵检测模型研究[J].山东师范大学学报(自然科学版),2006,21(4)：35-37.
[66]陈怡安Hilbert空间下的再生核理论研究[D].东北师范大学硕士学位论文,2010.
[67]Wu M, Ye J. A small sphere and large margin approach for novelty detection using training data with outliers [J]. Pattern Analysis and Machine Intelligence, IEEE Transactions on,2009,31(11):2088-2092.
[68]郑小霞,钱峰.高斯核支持向量机分类和模型参数选择研究[J].计算机工程与应用,2006,02：77-79.
[69]Guerrero-Curieses A, Conde-Pardo P, Landesa-Vazquez I, etal. On the performance of kernel methods for skin color segmentation [J]. EURASIP Journal on Advances in Signal Processing,2009.
[70]曾阳艳.基于PCA方法的人脸特征提取和检测[J].人工智能及识别技术,2008(1)：743-744.
[71]Hamdy A.Drowsy Driver Assistant System:Principal Component Analysis[Z]. GUC, Cairo,2007.
[72]孙涛,谷士文,费耀平.基于PCA算法的人脸识别方法研究比较[J].现代电子技术,2007(1)：111-114.
[73]李雪.利用PCA进行快速人脸检测[J].电子设计应用,2009(2)：56-58.
[74]肖政宏.基于PCA和GMM的图像分类算法[J].计算机工程与设计,2007,11(27)：1951-1952.
[75]李磊.基于PCA降维的SVM人脸快速检测方法[J].青岛科技大学学报(自然科学版),2010,31(5)：528-532.
[76]El-Bakry H M. New fast principal component analysis for real-time face detection[J]. Machine Graphics & Vision International Journal,2009,18(4):405-425.
[77]孙崇璇.基于PCA主成分分析的人脸检测实现与分析[J].云南民族大学学报(自然科学版),2010,19(6)：439-443.
[78]霍中华.基于肤色和Adaboost算法的人脸检测方法研究[D].华南理工大学硕士学位论文,2012.