多视角人脸检测技术的研究
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摘要
人脸检测是人脸识别等技术的研究基础,随着计算机技术的发展,人脸检测已经成为一个独立的研究课题,并且已经开始广泛应用到全新人机界面、基于内容的检索、基于目标的视频压缩、数字视频处理、视觉监测等许多领域。
本文围绕多视角的人脸检测展开讨论。通过分析前人的人脸检测研究,发现多是对正面图像进行人脸检测,或是有限角度的人脸检测,而对于多视角的人脸检测,有效的方法并不多。本文考虑到人脸检测中平面内旋转人脸和侧面人脸两种多视角情况,提出了一种多视角人脸检测方法。本文针对平面内旋转问题,在YCbCr色彩空间内建立肤色模型,经过处理确定人脸椭圆区域,利用基于灰度加权的主元分析算法进行角度校正,得到偏转校正后的人脸图像。针对侧面人脸问题,提出一种二维空间划分方法对样本角度空间进行划分,通过上下和左右2个方向的人脸旋转样本库来训练分类器,然后组合成并联分类器对偏转校正后的人脸图像进行人脸验证。进而设计实现了一个多视角人脸检测系统,实验结果表明,本文构建的分类器在人脸检测的检测率和误检率上均有较好的效果,该检测方法可以对多视角的人脸进行有效的检测。
Face detection is the basis of technologies like face recognition, with the development of Computer technology, face detection has become a independent research subject. Face detection has been used in many fields such as human computer inter face, searches based on content, visions inspection and so on.
This essay is centered of the constitution format of Multi-view in face detection. By analyzing the classifier built by former people, we find out that the current detection of human faces is mostly the frontal-view detection, and the view is limited, while the multi-view detection is still difficult, with few effective methods. Considering the rotate face and side face, this paper presents a multi-view face detection method. For rotating on the plane, we generate a human skin model in YCbCr color space. After treatment, elliptic color area of face is determined. Then, using the gray weighting of the principal component analysis algorithm for angle correction, and obtain the correction face image. For side face, through the face set of two directions of up or down and left or right, we train classifier, and combine them into parallel connection classifier for validation.This essay designed and implemented a Multi-view face detection system, which through comparison showed that the improved classifier have a better result both on detection rate and false positive rate. Experiments prove that this method is adaptive to different views face having a high face detection rate.
本文围绕多视角的人脸检测展开讨论。通过分析前人的人脸检测研究,发现多是对正面图像进行人脸检测,或是有限角度的人脸检测,而对于多视角的人脸检测,有效的方法并不多。本文考虑到人脸检测中平面内旋转人脸和侧面人脸两种多视角情况,提出了一种多视角人脸检测方法。本文针对平面内旋转问题,在YCbCr色彩空间内建立肤色模型,经过处理确定人脸椭圆区域,利用基于灰度加权的主元分析算法进行角度校正,得到偏转校正后的人脸图像。针对侧面人脸问题,提出一种二维空间划分方法对样本角度空间进行划分,通过上下和左右2个方向的人脸旋转样本库来训练分类器,然后组合成并联分类器对偏转校正后的人脸图像进行人脸验证。进而设计实现了一个多视角人脸检测系统,实验结果表明,本文构建的分类器在人脸检测的检测率和误检率上均有较好的效果,该检测方法可以对多视角的人脸进行有效的检测。
Face detection is the basis of technologies like face recognition, with the development of Computer technology, face detection has become a independent research subject. Face detection has been used in many fields such as human computer inter face, searches based on content, visions inspection and so on.
This essay is centered of the constitution format of Multi-view in face detection. By analyzing the classifier built by former people, we find out that the current detection of human faces is mostly the frontal-view detection, and the view is limited, while the multi-view detection is still difficult, with few effective methods. Considering the rotate face and side face, this paper presents a multi-view face detection method. For rotating on the plane, we generate a human skin model in YCbCr color space. After treatment, elliptic color area of face is determined. Then, using the gray weighting of the principal component analysis algorithm for angle correction, and obtain the correction face image. For side face, through the face set of two directions of up or down and left or right, we train classifier, and combine them into parallel connection classifier for validation.This essay designed and implemented a Multi-view face detection system, which through comparison showed that the improved classifier have a better result both on detection rate and false positive rate. Experiments prove that this method is adaptive to different views face having a high face detection rate.
引文
[1] H jelmas E.Face Detection:A Survey[J].Computer Vision and Image Understanding,2001(83):236-274.
[2] Yang MH,Kricgman D,Ahuja N. Detecting Faces in images:A survey[C].IEEE Trans Pattern Analysis and Machine Intelligence,2002,24(1):34-58.
[3]黄幅珍,苏剑波.人脸检测.上海交通大学出版社.2006(4).
[4] Weber M et al. View point invariant learning and detection of human heads. Proc. Conference on Automatic Face and Gesture Recognition,Grenble,France,2000.
[5] M H Yang,D Kriegman. Detection Using Multimodal Density Models. Computer Vision Understanding,2001,84:264-284.
[6]周德龙.人脸识别技术研究[D]:西北工业大学博士学位论文.2001,20-26.
[7]梁路宏.人脸检测研究综述[J].计算机学报.2002,25(5):449-458.
[8] H.A.Rowley,B.Shumeet, Neural Network-Based Face Detection IEEE Transactionson Pattern Analysisand Machine Intelligence,VOL.20 NO.1,pp.23-28,1998.
[9]杨宏梅.人脸检测算法的研究与实现[D]:兰州理工大学硕士学位论文.2009,[7].
[10] Rowley H,Baluja S,Kanade T. Rotation Invariant Neural Network-Based Face Detection. Proc. IEEE Conf. Computer Vision and Pattern Recognition,1998:38-44.
[11] Feraud R,Bernier O J ,Viallet J E et al. A Fast and Accurate Detector Based on Neural Networks. IEEE Trans.PAMI.2001,23(1):42-53.
[12] Schneiderman H, Kanade T. A Statistical Method for 3D Object Detection Applied to Faces and Cars. IEEE Conf on Computer Vision and Pattern Recognition,2000:1746.
[13] Yong min Li, Shao gang Gong, Jamie Sherrah and Heather Liddell. Support vector machine based multi-view face detection and recognition. Image and Vision Computing.2004,22(5):413-427.
[14] Yan Wang, Yang hua Liu, Lin mi Tao, Guangyou Xu. Real-Time Multi-View Face Detection and Pose Estimation in Video Stream. Pattern Recognition, 2006, 4:354-357.
[15] Viola P., Jones M. Rapid object detection using a boosted cascade of simple features[C]//Proc. Of IEEE Conference Computer Vision and Patten Recognition,Kauai,Hawaii,USA,2001:511-518.
[16]田源,于凤芹.人脸检测方法综述[J].计算机安全,2008,10.
[17]李刚,高政.人脸自动识别方法综述[J].计算机应用研究,2003,Vols:4-9.
[18]吴蓓.多视角人脸检测方法的研究[D]:大连理工大学硕士论文,2006,[12].
[19]卢春雨,张长水,闻方等.基于区域特征的快速人脸检测法[J].清华大学学报(自然科学版)[ J],1999,39(l):201-10.
[20] Lanitis A, Taylor C J,and Cootes T F. An automatic face identification system using flexible appearance models. Image and Vision Computing.1995,13(5):393-401.
[21]姜军,张桂林,一种基于知识的快速人脸检测方法[J].中国图像图形学报,2002,1(1).
[22]梁路宏.人脸检测与跟踪研究[D].清华大学,2001:9-13
[23]王琼.人脸与人脸特征检测技术研究[D],南京理工大学,2007,[7].
[24] K C Yow , R Cipolla , Feature-based Human Face Detection. Image and Vision Computing.1997,15(9):713-735.
[25] K C Yow,R Cipolla ,Enhancing Human Face Detection Using Motion and Active contours,3rd Asian Conf. Computer Vision.1998:515-522.
[26]王良民,张建明,詹永照,等.人脸检测研究现状和发展[J].江苏大学学报(自然科学版),2003,24(3):75-79 .
[27]包力,基于特征与模板的人脸检测研究算法与应用[D],大连海事大学硕士学位论文,2006,[3].
[28]孔潇,沈兰荪,毋立芳.一种基于特征提取和模板匹配的人脸检测算法[J].测控技术,2003,22(5).
[29]王科俊,姚向辉.人脸图像检测与识别方法综述[J].自动化技术与应用,2004,23(l2): 5-8,15.
[30]陈锻生,刘政凯.肤色检测技术综述[J].计算机学报,2006,29(2):194-207
[31]江珂,王玲.运用肤色信息和模板匹配的彩色人脸检测[J].中国测试技术,2006,32(1): 53-55.
[32]刘丽华.人脸识别方法综述[J].云南师范大学学报,2005,25(6):13-18.
[33] Burges C.J.C.A tutorial on support vector machines for pattern recognition. Knowledge Discovery and Data Mining,2:121-167,1998.
[34]刘刚,张洪刚,郭军.不同训练样本对识别系统的影响.计算机学报, 28(11):1923-1928, 2005.
[35] Jenkins R.,Burton A.M.100%accuracy in automatic face recognition. Science, 319(25):435,2008.
[36]陈武凡.小波分析及其在图像处理中的应用[M].科学出版社.2002.
[37] Lienhart R, Kuranov A, V Pisarevsky. Empirical analysis of detection cascades of boosted classifiers for rapid object detection. DAGM’03 25th Pattern Recognition Symposium 2003.
[38] Yoav Freund and Robert E. Schapire, A short introduction to boosting, Journal of Japanese Society for Artificial Intelligence, 14(5):771-780, September,1999.
[39] R.E.Schapire,Y.Singer.Improved boosting algorithms using Confidence-rated predictions Machine Learning, 1999, 37(3). 297-336.
[40]涂承胜,陆玉昌,Boosting理论基础[J],计算机科学2004Vo1.31NO.10.
[41] Vezhnevets Vladimir, Sazonov Vassili, Alla Andreeva. A Survey on Pixel-Based skin Color Detection Techniques [C]//Proceedings of the Graphicon 2003. 2003, 14(1): 85-92.
[42] Hsu ReinLien , Mohamed , Jain Anil K. Face detection in color images[J] . IEEE Trans on Pattern Analysis and Machine Intelligence, 2002, 24(5) : 696-706.
[43] Zhou Delong, Zong Junbin. Minimum Error Thresholding Based on Two Dimensional Histogram. 2009 WRI World Congress on Computer Science and Information Engineering. 2009:169-175.
[44]吴证,周越,杜春华,袁泉,戈新良.彩色图像人脸特征点定位算法研究[J].电子学报, 2008,(02):309-313.
[45] Krinidis, S, Chatzis, V. Fuzzy Energy-Based Active Contours. IEEE Transactions on Image Processing, 2009 ,18(12):2747-2755.
[46]阮书敏,复杂背景下的多姿态人脸检测技术研究[D],研究生学位论文福州大学,2006,[1].
[2] Yang MH,Kricgman D,Ahuja N. Detecting Faces in images:A survey[C].IEEE Trans Pattern Analysis and Machine Intelligence,2002,24(1):34-58.
[3]黄幅珍,苏剑波.人脸检测.上海交通大学出版社.2006(4).
[4] Weber M et al. View point invariant learning and detection of human heads. Proc. Conference on Automatic Face and Gesture Recognition,Grenble,France,2000.
[5] M H Yang,D Kriegman. Detection Using Multimodal Density Models. Computer Vision Understanding,2001,84:264-284.
[6]周德龙.人脸识别技术研究[D]:西北工业大学博士学位论文.2001,20-26.
[7]梁路宏.人脸检测研究综述[J].计算机学报.2002,25(5):449-458.
[8] H.A.Rowley,B.Shumeet, Neural Network-Based Face Detection IEEE Transactionson Pattern Analysisand Machine Intelligence,VOL.20 NO.1,pp.23-28,1998.
[9]杨宏梅.人脸检测算法的研究与实现[D]:兰州理工大学硕士学位论文.2009,[7].
[10] Rowley H,Baluja S,Kanade T. Rotation Invariant Neural Network-Based Face Detection. Proc. IEEE Conf. Computer Vision and Pattern Recognition,1998:38-44.
[11] Feraud R,Bernier O J ,Viallet J E et al. A Fast and Accurate Detector Based on Neural Networks. IEEE Trans.PAMI.2001,23(1):42-53.
[12] Schneiderman H, Kanade T. A Statistical Method for 3D Object Detection Applied to Faces and Cars. IEEE Conf on Computer Vision and Pattern Recognition,2000:1746.
[13] Yong min Li, Shao gang Gong, Jamie Sherrah and Heather Liddell. Support vector machine based multi-view face detection and recognition. Image and Vision Computing.2004,22(5):413-427.
[14] Yan Wang, Yang hua Liu, Lin mi Tao, Guangyou Xu. Real-Time Multi-View Face Detection and Pose Estimation in Video Stream. Pattern Recognition, 2006, 4:354-357.
[15] Viola P., Jones M. Rapid object detection using a boosted cascade of simple features[C]//Proc. Of IEEE Conference Computer Vision and Patten Recognition,Kauai,Hawaii,USA,2001:511-518.
[16]田源,于凤芹.人脸检测方法综述[J].计算机安全,2008,10.
[17]李刚,高政.人脸自动识别方法综述[J].计算机应用研究,2003,Vols:4-9.
[18]吴蓓.多视角人脸检测方法的研究[D]:大连理工大学硕士论文,2006,[12].
[19]卢春雨,张长水,闻方等.基于区域特征的快速人脸检测法[J].清华大学学报(自然科学版)[ J],1999,39(l):201-10.
[20] Lanitis A, Taylor C J,and Cootes T F. An automatic face identification system using flexible appearance models. Image and Vision Computing.1995,13(5):393-401.
[21]姜军,张桂林,一种基于知识的快速人脸检测方法[J].中国图像图形学报,2002,1(1).
[22]梁路宏.人脸检测与跟踪研究[D].清华大学,2001:9-13
[23]王琼.人脸与人脸特征检测技术研究[D],南京理工大学,2007,[7].
[24] K C Yow , R Cipolla , Feature-based Human Face Detection. Image and Vision Computing.1997,15(9):713-735.
[25] K C Yow,R Cipolla ,Enhancing Human Face Detection Using Motion and Active contours,3rd Asian Conf. Computer Vision.1998:515-522.
[26]王良民,张建明,詹永照,等.人脸检测研究现状和发展[J].江苏大学学报(自然科学版),2003,24(3):75-79 .
[27]包力,基于特征与模板的人脸检测研究算法与应用[D],大连海事大学硕士学位论文,2006,[3].
[28]孔潇,沈兰荪,毋立芳.一种基于特征提取和模板匹配的人脸检测算法[J].测控技术,2003,22(5).
[29]王科俊,姚向辉.人脸图像检测与识别方法综述[J].自动化技术与应用,2004,23(l2): 5-8,15.
[30]陈锻生,刘政凯.肤色检测技术综述[J].计算机学报,2006,29(2):194-207
[31]江珂,王玲.运用肤色信息和模板匹配的彩色人脸检测[J].中国测试技术,2006,32(1): 53-55.
[32]刘丽华.人脸识别方法综述[J].云南师范大学学报,2005,25(6):13-18.
[33] Burges C.J.C.A tutorial on support vector machines for pattern recognition. Knowledge Discovery and Data Mining,2:121-167,1998.
[34]刘刚,张洪刚,郭军.不同训练样本对识别系统的影响.计算机学报, 28(11):1923-1928, 2005.
[35] Jenkins R.,Burton A.M.100%accuracy in automatic face recognition. Science, 319(25):435,2008.
[36]陈武凡.小波分析及其在图像处理中的应用[M].科学出版社.2002.
[37] Lienhart R, Kuranov A, V Pisarevsky. Empirical analysis of detection cascades of boosted classifiers for rapid object detection. DAGM’03 25th Pattern Recognition Symposium 2003.
[38] Yoav Freund and Robert E. Schapire, A short introduction to boosting, Journal of Japanese Society for Artificial Intelligence, 14(5):771-780, September,1999.
[39] R.E.Schapire,Y.Singer.Improved boosting algorithms using Confidence-rated predictions Machine Learning, 1999, 37(3). 297-336.
[40]涂承胜,陆玉昌,Boosting理论基础[J],计算机科学2004Vo1.31NO.10.
[41] Vezhnevets Vladimir, Sazonov Vassili, Alla Andreeva. A Survey on Pixel-Based skin Color Detection Techniques [C]//Proceedings of the Graphicon 2003. 2003, 14(1): 85-92.
[42] Hsu ReinLien , Mohamed , Jain Anil K. Face detection in color images[J] . IEEE Trans on Pattern Analysis and Machine Intelligence, 2002, 24(5) : 696-706.
[43] Zhou Delong, Zong Junbin. Minimum Error Thresholding Based on Two Dimensional Histogram. 2009 WRI World Congress on Computer Science and Information Engineering. 2009:169-175.
[44]吴证,周越,杜春华,袁泉,戈新良.彩色图像人脸特征点定位算法研究[J].电子学报, 2008,(02):309-313.
[45] Krinidis, S, Chatzis, V. Fuzzy Energy-Based Active Contours. IEEE Transactions on Image Processing, 2009 ,18(12):2747-2755.
[46]阮书敏,复杂背景下的多姿态人脸检测技术研究[D],研究生学位论文福州大学,2006,[1].