一种基于改进YUV的人脸检测方法
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摘要
人脸检测是确定人脸的位置、大小、数目的图像处理过程。人脸检测是人脸识别、人机交互、智能视觉监控等工作的前提。随着计算机应用的普及、性能提高以及图像和模式识别领域的研究逐步成熟,对人脸检测的研究也将受到越来越多的重视。虽然近几十年来出现了大量的人脸检测的算法,但检测率、检测速度以及算法鲁棒性等方面还是不够理想。因此本文对人脸检测进行了深入的研究,主要工作在如下三个方面:
1.本文在灰度空间提出了一种利用眼睛和鼻子的灰度特征和几何特征的人脸检测方法。选取眼睛和鼻子作为特征点,构造一个三角的特征模型。另外,此方法对候选特征图像采用逐步改变分块大小的方法进行搜索,得到独立的特征点,并利用人脸结构特点的先验知识建立模型的搜索策略。实验表明,此方法能迅速准确的从复杂背景中检测出人脸,而且对多人脸同样有效。
2.根据肤色色度的分配比例,本文提出了一种改进YCbCr的肤色检测方法,用Cg分量代替Cb分量,实验表明在改进的颜色空间内,肤色投影到CrCg平面内了更好得聚类效果。
3.在肤色背景下,利用肤色和唇色在YUV空间分布特点,变换YUV空间的坐标轴,增大唇色和肤色V分量上的差异。提取的唇色的质心和旋转方向,根据人脸几何特征的先验知识建立人脸定位模型。实验表明唇色定位人脸算法简单,速度快,更具实效性,对旋转的人脸同样有效。
Face detection is an image processing course, ascertaining face's location, size and amount in an image. And face detection is the precondition of face recognition, human-computer interface and intelligent scene supervision system. Research on face detection will be paid more attention, with computer application being popular and its capabilities improved and researches growing up in image processing and mode recognition field. There are a lot of arithmetics of face detection in the past decades. But the rate, precision and robustness of arthmetics are not ideal. The research works in this paper is as following.
The key problem of face detecting re is extracting features in gray space.. In this paper, we proposed a method of Feature Model Based Face Detecting. It extracts features from eyes and nose, and then constructs a triangular feature mold component with eyes and nose. The search is taken in the candidate feature image by changing the size of blocks step by step. This processing can obtain separate points of feature mold. And finally proposed a strategy based on the knowledge about facial structure/distribution to search the feature mold. Experimental results prove that this method can rapidly and accurately detect face in an image with complex background. Furthermore, it can perform well for multiple faces.
Considering the proportion of different chroma of skin color, we proposed a method of skin detection based on improved YCbCr color space. Instead Cb of Cg. Experimental results prove that skin pixel have better clustering in improved YCbCr color space.
Under simple skin underground, change axis on YUV color space, in order to increasing distribution deference between skin sample and lip sample, lastly, using the geometry feature of lip on human face to locate face. Experimental results prove that the method can provide lower false rate,and achieve good detection performance in multi-face image. Lower complexity suits to reality.
1.本文在灰度空间提出了一种利用眼睛和鼻子的灰度特征和几何特征的人脸检测方法。选取眼睛和鼻子作为特征点,构造一个三角的特征模型。另外,此方法对候选特征图像采用逐步改变分块大小的方法进行搜索,得到独立的特征点,并利用人脸结构特点的先验知识建立模型的搜索策略。实验表明,此方法能迅速准确的从复杂背景中检测出人脸,而且对多人脸同样有效。
2.根据肤色色度的分配比例,本文提出了一种改进YCbCr的肤色检测方法,用Cg分量代替Cb分量,实验表明在改进的颜色空间内,肤色投影到CrCg平面内了更好得聚类效果。
3.在肤色背景下,利用肤色和唇色在YUV空间分布特点,变换YUV空间的坐标轴,增大唇色和肤色V分量上的差异。提取的唇色的质心和旋转方向,根据人脸几何特征的先验知识建立人脸定位模型。实验表明唇色定位人脸算法简单,速度快,更具实效性,对旋转的人脸同样有效。
Face detection is an image processing course, ascertaining face's location, size and amount in an image. And face detection is the precondition of face recognition, human-computer interface and intelligent scene supervision system. Research on face detection will be paid more attention, with computer application being popular and its capabilities improved and researches growing up in image processing and mode recognition field. There are a lot of arithmetics of face detection in the past decades. But the rate, precision and robustness of arthmetics are not ideal. The research works in this paper is as following.
The key problem of face detecting re is extracting features in gray space.. In this paper, we proposed a method of Feature Model Based Face Detecting. It extracts features from eyes and nose, and then constructs a triangular feature mold component with eyes and nose. The search is taken in the candidate feature image by changing the size of blocks step by step. This processing can obtain separate points of feature mold. And finally proposed a strategy based on the knowledge about facial structure/distribution to search the feature mold. Experimental results prove that this method can rapidly and accurately detect face in an image with complex background. Furthermore, it can perform well for multiple faces.
Considering the proportion of different chroma of skin color, we proposed a method of skin detection based on improved YCbCr color space. Instead Cb of Cg. Experimental results prove that skin pixel have better clustering in improved YCbCr color space.
Under simple skin underground, change axis on YUV color space, in order to increasing distribution deference between skin sample and lip sample, lastly, using the geometry feature of lip on human face to locate face. Experimental results prove that the method can provide lower false rate,and achieve good detection performance in multi-face image. Lower complexity suits to reality.
引文
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[3]梁路宏,艾海舟,徐光佑.人脸检测研究综述.计算机学报,2002,25(5):449-458.
[4]章毓晋.中国图像工程:2003.中国图象图形学报,A辑,2004,9(5):513-531.
[5] Paul Viola, Michael Jones. Rapid Object Detection using a Boosted Cascade of Simple Features[C] .IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'01),2001,1:511.
[6] Lienhart R, Maydt J. An Extended Set of Haar-like Features for Rapid Object Detection[C] .IEEE ICIP ,2002, 1:900-903.
[7] Lienhart R, Kuranov A, V Pisarevsky. Empirical analysis of detection cascades of boosted classifiers for rapid object detection. 25th Pattern Recognition Symposium 2003:297-304.
[8] Lienhart R, Liang L, and Kuranov A. A detector tree of boosted classifier for real time object detection and tracking. International Conference on Multimedia & Expo (ICME’03),2003,2(6-9).
[9] Viola P. Fast and Robust Classification using Asymmetric AdaBoost and a Detector Cascade(c). NIPS’2001, 2001: 1311-1318.
[10]Zhang ZQ, Li M, Li SZ, Zhang HJ. Multi-view face detection with floatboost.WACV’02,2002:184-188.
[11]Sahbi H. Coarse-to-Fine Support Vector Machines for Hierarchical Face Detection.(PhD.Thesis), University of Versailles, 2003.
[12]Sahbi H, Boujemaa N. Coarse-to-Fine Support Vector Classifiers for Face Detection. International Conference on Image Processing(ICPR’02),2002,3:30359
[13]Sun QB, Huang WM, Wu JK. Face detection based on color and local symmetry information[C]. Proc Conference Automatic Face and Gesture Recognition, 1998:130-135
[14]陶霖密,彭振云,徐光枯.人体的肤色特征软件学报,2001,12(7):1032-1040.
[15]彭波,崔永普,黄丹霞,吕小晴.基于肤色和唇色信息的人脸检测方法的研究.《计算机工程与设计》2005年26(6):1500-1502
[16]Hongxun Yao, Wen Gao. Face detection and location based on skin chrominance and lip Chrominance transformation from color images[C]. IEEE Conference on Pattern Recognition.2001,34: 1555-1564
[17]Carlos H. Morimoto ,Marcio C.. On the usability of gesture interfaces in virtual reality environments[C]. Proceedings of the 2005 Latin American conference on Human-computer interaction,2005,124:100– 108.
[18]Flickner, M. Sawhney, H. Niblack, et al. Query by image and video content: the QBIC system[M].multimedia computing and networking, Year of Publication ,2001: 255 - 264
[19]Ying Dai, Yasuaki Nakano. Face-texture model based on SGLD and its application in facedetection in a color scene. Pattern Recognition, 1996,29(6):1007-1017.
[20]R.Feraud, O.Bernier, D.Collobert. Constrained generative model applied to face detection. Neural Processing Letters, 1997,5(2):11-99.Chen Ying-Nong, Ho Gang-Feng,Fan, Kuo-Chin, Han, Chin-Chuan. Face Detection and Authentication in a Large Space[M].Third International Conference on Intelligent Information Hiding and Multimedia Signal Processing, 2007, 2:423-426.
[21]姚鸿勋,刘明宝,高文等.基于彩色图像的色系坐标变换的面部定位与跟踪法[J].计算机学报.2000,23(2):158-165.
[22]F.Samaria,S.Young. HMM-based architecture for face identification.Image and vision computing,1994,12(8):537- 543.
[23]SAMARIA F. YOUNG S. HMM-based architecture for face identification, Image and vision ,1994,12(8):537-543
[24]Bernd Heisele, Thomas Serre ,T. Poggio.A Component-based Framework for Face Detection and Identification.International[J].Journal of Computer Vision,2007,74(2).
[25]M. Zobel, A. Gebhard, D. Paulus, et al.Robust Facial Feature Localization by Coupled Features [C].Proceeding of the Fourth IEEE International Conference on Automatic Face and Gesture Recognition, 2000:2-7.
[26]C Nastar, N Ayache. Frequency-based nonrigid motion analysis: application to four dimensional medical images. IEEE Transaction on Patten Analysis and Machine Intelligence,1996,18(11).
[27]S. Charles Brubaker, Atlanta,Jianxin Wu .On the Design of Cascades of Boosted Ensembles for Face Detection[J].International Journal of Computer Vision ,2008, 77:65– 86.
[28]Papageorgiou C.P, Oren M. Poggio T. A general framework for object detection[C]. Computer Vision, Sixth International Conference on, 1998:: 555-562
[29]Oren M, Papageorgiou C, Sinha P, et al. Pedestrian detection using wavelet templates. Computer Vision and Pattern Recognition[C]. IEEE Computer Society Conference, 1997:193-199.
[30]Garcia C., Delakis M. Convolutional Face Finder: A Neural Architecture for Fast and Robust Face Detection[J]..IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE ,2004.26(11):1408-1423.
[31]P.Vadakkepat, P.Lim, L.De Silva.Multimodal Approach to Human-Face Detection and Tracking[J]. IEEE Transactions on Industrial Electronics, 2008,55(3),1385-1393.
[32]Georgios Tziritas, Eftichis Sifakis, Christophe Garcia .Bayesian Level Sets for Image Segmentation[J] . Visual Communication and Image Representation,2002, 13:44-64.
[33]T.kondo, Hong Yan. Automatic human face detection and recognition under non-uniform illumination[J].Pattern recognition.1999,32:1707-1718 .
[34]Rein-Lien Hsu, Anil K.Jain. Face Detection in Color Images[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence. 2002, 24(5):696-706.
[35]Ing-Sheen Hsieh, Kuo-Chin Fan. A statistic approach to the detection of human faces in color nature scene. Pattern Recognition. 2002, 35(7):1583-1596.
[36]Chang Huang, Haizhou Ai,Yuan Li .High-Performance Rotation Invariant Multiview Face Detection[J].IEEE transactions on pattern analysis and machine intelligence.2007.29(4),671-686.
[37]J. D. Brand, Dr. J. S. D. Mason. A Skin Probability Map and Its Use in Face Detection[J].Proceedings of International Conference on Images Processing.2001,1(7-10):1034-1037.
[38]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
[39]Phung S. Bouzerdoum A.,Chai D..Skin segmentation using color pixel classification: Analysis and comparison[J] . IEEE Transactions on Pattern Analysis and Machine intelligence,20O5,27(1):149-154.
[40]Storring M.Computer vision and human skin color [Ph.D.dissertation].Aalborg University,Denmark :Computer Vision and Media Technology Laboratory ,2004 , http : / /www.cvmt.dk/～mst
[41]Yang M.H.,Kriegman D.,Ahuja N..Detecting faces in images:A survey[J] .IEEE Transactions on Pattern Analysis and Machine Intelligence,2002,24(1):34-58.
[42]Zhao W .,Chellapa R.,Philips P.J.,etal.Face recognition: Literature survey. ACM Computing Survey, 2003,35(4):399-458.
[43]Oliver N.,Pentland A.,Berard F.A real—time face and lips tracker with facial expression recognition. Pattern Recognition,2000,33:l369-1382.
[44]McKenna S.J.,Morrison K..A comparison of skin history and trajectory—based representation schemes for the recognition of user-specified gestures[J].Pattern Recognition,2004,37(5):999-1009.
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