基于彩色图像的人脸检测与识别技术研究
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摘要
由于人脸检测识别技术在安全部门、电视会议、身份鉴别、数字监控等领域的应用越来越广泛,它作为一项极具发展潜力的生物特征识别技术,已成为近年来科学研究的热点,也已引起了研究者们越来越多的关注。
本文针对彩色图像中人脸的检测和识别技术进行了详细的研究,提出了人眼中心定位和简化人脸识别的新方法,并对现有算法进行了有效的改进。本文主要包括两部分的内容,一部分是人脸检测,另一部分是人脸识别。
将人脸检测分为粗定位和精确定位两部分。在讨论及分析肤色模型概念及原理的基础上给出了基于肤色模型的人脸分割方法,从而实现了人脸的粗定位。在Canny算子边缘检测的基础上,提出了一种人眼中心的定位方法,在确定眼睛大致区域后,利用圆的几何特性在边缘图像中寻找眼睛中心点。综合运用亮度和色度信息定位鼻子和嘴巴中心点以及其它特征点,并将三次B样条曲线用于鼻子轮廓的拟合。最终实现了人脸的精确定位,排除了脖子、服饰等干扰因素。
在人脸识别部分,基于隐马尔可夫模型,提出了一种简化观察序列提取过程的方法。通过小波变换压缩原始图像的数据,利用人脸检测阶段所得到的结果缩小采样范围,定义恰当尺寸的采样窗口,直接以人脸采样区域的灰度均值作为特征,形成一系列观察序列,从而简化人脸隐马尔可夫模型的训练过程,减少识别人脸的时间。
通过大量实验验证本文所提出的人脸检测及人脸识别方法的准确性和可行性。
Human face detection and recognition has applied more and more on the area such as security system, human ID, digital surveillance and so on. As a biologic technology which possesses great developable potential, it has become one of the most active research topics, and more and more investigators put their attention on this topic.
The human face detection and recognition of color image is the studied object. In this paper some new methods have been presented, and also improve on some existed methods. This paper has two main parts, one is the face detection, and the other is the face recognition.
In the part of face detection, the conception and principle of skin color model have been introduced first. Then the skin color area can be segmented and the approximate face region can be located according to the skin color model. After comprising the edge detection operators which can be used on face detection, the Canny operator is chose to detect the edge information. One eyes' positioning method is introduce. The principle is detecting circles in the eyes regions with the geometry property of circle. Then the methods of positioning the nose and mouth are introduced which used the luminance and chroma information. The 3-order based spline curve is use to simulate the border of nose. Then the exacted region of face can be located, which eliminates the factors such as neck.
In this paper, the face recognition implementation is based on the HMM.A predigest method of observation sequence is proposed. Using the wavelet transform to compress the information of image. Define seemliness sampling window to extract gray as the feature in the compress region based on the results of face detection. And then the process of training HMM can be predigested.
The lots of experimental results prove the validity and accurately of the algorithms of face detection and face recognition presented in this paper.
本文针对彩色图像中人脸的检测和识别技术进行了详细的研究,提出了人眼中心定位和简化人脸识别的新方法,并对现有算法进行了有效的改进。本文主要包括两部分的内容,一部分是人脸检测,另一部分是人脸识别。
将人脸检测分为粗定位和精确定位两部分。在讨论及分析肤色模型概念及原理的基础上给出了基于肤色模型的人脸分割方法,从而实现了人脸的粗定位。在Canny算子边缘检测的基础上,提出了一种人眼中心的定位方法,在确定眼睛大致区域后,利用圆的几何特性在边缘图像中寻找眼睛中心点。综合运用亮度和色度信息定位鼻子和嘴巴中心点以及其它特征点,并将三次B样条曲线用于鼻子轮廓的拟合。最终实现了人脸的精确定位,排除了脖子、服饰等干扰因素。
在人脸识别部分,基于隐马尔可夫模型,提出了一种简化观察序列提取过程的方法。通过小波变换压缩原始图像的数据,利用人脸检测阶段所得到的结果缩小采样范围,定义恰当尺寸的采样窗口,直接以人脸采样区域的灰度均值作为特征,形成一系列观察序列,从而简化人脸隐马尔可夫模型的训练过程,减少识别人脸的时间。
通过大量实验验证本文所提出的人脸检测及人脸识别方法的准确性和可行性。
Human face detection and recognition has applied more and more on the area such as security system, human ID, digital surveillance and so on. As a biologic technology which possesses great developable potential, it has become one of the most active research topics, and more and more investigators put their attention on this topic.
The human face detection and recognition of color image is the studied object. In this paper some new methods have been presented, and also improve on some existed methods. This paper has two main parts, one is the face detection, and the other is the face recognition.
In the part of face detection, the conception and principle of skin color model have been introduced first. Then the skin color area can be segmented and the approximate face region can be located according to the skin color model. After comprising the edge detection operators which can be used on face detection, the Canny operator is chose to detect the edge information. One eyes' positioning method is introduce. The principle is detecting circles in the eyes regions with the geometry property of circle. Then the methods of positioning the nose and mouth are introduced which used the luminance and chroma information. The 3-order based spline curve is use to simulate the border of nose. Then the exacted region of face can be located, which eliminates the factors such as neck.
In this paper, the face recognition implementation is based on the HMM.A predigest method of observation sequence is proposed. Using the wavelet transform to compress the information of image. Define seemliness sampling window to extract gray as the feature in the compress region based on the results of face detection. And then the process of training HMM can be predigested.
The lots of experimental results prove the validity and accurately of the algorithms of face detection and face recognition presented in this paper.
引文
1.何坤.人脸识别理论关键技术的研究[D].成都:四川大学,2007
2.Samal A,Iyengar P A.Automatic recognition and analysis of human faces and facial expressions:a survey[J],Pattern Recognition,1992,25(1):65-77
3.Chellappa R,Wilson C L and Simhey S.Human and machine recognition of faces:a survey[J],Proceedings of the IEEE,1995,83(5):705-740
4.Parke F I.Computer generated animation of faces[D].Salt Lake City:University of Utah,1972
5.Adini Y.The problem of compensating for changes in illumination direction[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,1997,19(7):721-732
6.Turk M.Eigenfaces for recognition[J].Cognitive Neuroscience,1991,3(1):71-86
7.彭辉,张长水等.基于K-L变换的人脸自动识别方法[J].清华大学学报(自然科学版),1997.37(3):67-70
8.Juell P,Mstdh R.A hierarchical neural network for human face detection[J].Pattern Recognition,1996,29(5):781-787
9.Intrator N.Face recognition using a hybrid supervised/unsupervised neural network[J].Pattem Recognition Letters,1996,17(1):67-76
10.高西奇.基于小波变换的主元分析人脸图像识别[J].东南大学学报1996,26(2):137-141
11.王芳.基于五官特征定位的人脸识别技术在防盜门中的应用[D].青岛:中国海洋大学,2005
12.赵丽红.人脸的检测和识别算法的研究与实现[D].沈阳:东北大学,2006
13.梁路宏,艾海舟等.基于多模板匹配的单人脸检测[J].中国图象图形学报1999,12(1):823-830
14.T.Agui.Extraction of face recognition from monochromatic photographs using neural networks[J].Processing of the 2nd Int'l Conf.Automation,Robotics,and Computer Vision,1992,18(8):1-5
15.M.Propp and A.Sarnal.Artificial Neural Network Architectures face detection[J]. Intell.Eng.Systems Artificial Neural Networks,1992,(2),535-540
16.E.Osuna.Training Support vector machines:An application to face detection[J].Processing of IEEE conference on computer vision and pattern recognition,1997,130-1361
7.K WANG IN KIM.Face recognition using support vector machines with local correlation kernels[J].International journal of pattern recognition and artificial intelligence,2002,16(1):97-111
18.P.N.Belhumeur.Using class specific linearmachine intelligence[J].IEEE Transactions on Pattern Analysis,1997,19(7):711-720
19.M.S.Bartlett.Pace recognition by independent component analysis[J].IEEE transactions on neural networks,2002,13(6):1450-1464
20.Tang C Y,Chen Z.Automatic Detection and Tracking of Human Heads Using an Active Stereo Vision System[J].International Journal of Pattern Recognition and Artificial Intelligence,2000,14(2):137-166
21.P.G Luebbers,O.A.Uwechue,A.S.Pandya.A neural network based facial recognition system[J].Processing of SPIF,1994,22(43):595-606
22.Lin S H,Kung S Y.Face recognition/detection by probabilistic decision based neural network[J].IEEE Trans on Neural Networks,1997.8(1):14-132
23.M.Lades,J.C.Vorbruggen.Distortion invariant object recognition in the dynamic link architecture[J].IEEE Trans.on Computer,1993,42(3):300-311
24.L.Wiskott.Recognizing faces by dynamic link matching[J].Neuroimage,1996,4(3):514-518
25.章毓晋.图像处理和分析(图像工程上册)[M].北京:清华大学出版社,2003:181-182
26.张宏林.Visual C++数字图像模式识别技术及工程实践[M].北京:人民邮电出版社,2003:363-421
27.Rein-Lien Hsu,Mohamed Abdel-Mottaleb.Face detection in color images[J].IEEE Trans.Pattern Anal.Mach.Intell,2002,24(5):696-706
28.J Kovac P Peer,F Solina.Human skin colour clustering for face detection[J].Faculty of computer and information science,2003,2(22-24):144-14.8
29.何斌,马天予,王运坚.Visual C++数字图像处理LM].北京:人民邮电出版社,2002:7-8
30.Sankur B,Sezgin M.A survey over image thresh holding techniques and quantitative performance evaluation[J].Journal of Electronic Imaging,2004,13(1):146-165
31.郑南宁.机器视觉与模式识别[M].北京:国防工业出版社,1998:66-69
32.朱夏君.人脸识别的研究及眼睛定位算法[D].杭州:浙江工业大学,2003
33.张杰,杨晓飞,赵瑞莲.基于Hough变换圆检测的人眼精确定位方法[J].计算机工程与应用,2005,27(2):43-44
34.孙亦南,刘伟军,王越超等.一种用于圆检测的改进Hough变换方法[J].计算机工程与应用,2003,39(20):35-37
35.孙家广.计算机图形学[M].清华大学出版社,2006:308-318
36.梁路宏,艾海舟,徐光祐,张钹.人脸检测研究综述[J].计算机学报,2002,25(5):449-456
37.Turk M,Pentland A P.Eigenfaces for recognition[J].Journal of Cognitive Neuroscience,1991,3(1):71-86
38.Rabiner L R.A tutorial on hidden Markov models and selected applications in speech recognition[J].Proceedings of the IEEE,1989,77(2):257-286
39.Nefian A.A hidden Markov model based approach for face detection and recognition[D].Georgia:Georgia Institute of Technology,1999
40.Eickeler S,M(u|¨)llerS,Rigoll GRecognition of JPEG compressed face images based on statistical methods[J].Image and Vision Computing,2000,18(4):279-287
41.Othman.A simplified second-order HMM with application to face recognition[J].IEEE International Symposium on Circuits and Systems,2001,2(6-9):161-164
42.Samaria F,Yong S.HMM-based architecture.for face identification[J].Image and Vision Computing,1994,12(8):537-543
43.Nefian A V.Hidden markov models for face recognition[J].Proceedings of the IEEE International Conference on Acoustics,Speech and Signal Processing,1998,5(1):2721-2724
44.张旭东.图像编码基础和小波压缩技术[M].北京:清华大学出版社,2004:165-208
45.徐毅琼等.基于隐马尔可夫模型的自动人脸识别方法[J].计算机应用.2004.24(12):227
46.李杰.隐马尔可夫模型的研究及其在图像识别中的应用[D].北京:清华大学,2004
2.Samal A,Iyengar P A.Automatic recognition and analysis of human faces and facial expressions:a survey[J],Pattern Recognition,1992,25(1):65-77
3.Chellappa R,Wilson C L and Simhey S.Human and machine recognition of faces:a survey[J],Proceedings of the IEEE,1995,83(5):705-740
4.Parke F I.Computer generated animation of faces[D].Salt Lake City:University of Utah,1972
5.Adini Y.The problem of compensating for changes in illumination direction[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,1997,19(7):721-732
6.Turk M.Eigenfaces for recognition[J].Cognitive Neuroscience,1991,3(1):71-86
7.彭辉,张长水等.基于K-L变换的人脸自动识别方法[J].清华大学学报(自然科学版),1997.37(3):67-70
8.Juell P,Mstdh R.A hierarchical neural network for human face detection[J].Pattern Recognition,1996,29(5):781-787
9.Intrator N.Face recognition using a hybrid supervised/unsupervised neural network[J].Pattem Recognition Letters,1996,17(1):67-76
10.高西奇.基于小波变换的主元分析人脸图像识别[J].东南大学学报1996,26(2):137-141
11.王芳.基于五官特征定位的人脸识别技术在防盜门中的应用[D].青岛:中国海洋大学,2005
12.赵丽红.人脸的检测和识别算法的研究与实现[D].沈阳:东北大学,2006
13.梁路宏,艾海舟等.基于多模板匹配的单人脸检测[J].中国图象图形学报1999,12(1):823-830
14.T.Agui.Extraction of face recognition from monochromatic photographs using neural networks[J].Processing of the 2nd Int'l Conf.Automation,Robotics,and Computer Vision,1992,18(8):1-5
15.M.Propp and A.Sarnal.Artificial Neural Network Architectures face detection[J]. Intell.Eng.Systems Artificial Neural Networks,1992,(2),535-540
16.E.Osuna.Training Support vector machines:An application to face detection[J].Processing of IEEE conference on computer vision and pattern recognition,1997,130-1361
7.K WANG IN KIM.Face recognition using support vector machines with local correlation kernels[J].International journal of pattern recognition and artificial intelligence,2002,16(1):97-111
18.P.N.Belhumeur.Using class specific linearmachine intelligence[J].IEEE Transactions on Pattern Analysis,1997,19(7):711-720
19.M.S.Bartlett.Pace recognition by independent component analysis[J].IEEE transactions on neural networks,2002,13(6):1450-1464
20.Tang C Y,Chen Z.Automatic Detection and Tracking of Human Heads Using an Active Stereo Vision System[J].International Journal of Pattern Recognition and Artificial Intelligence,2000,14(2):137-166
21.P.G Luebbers,O.A.Uwechue,A.S.Pandya.A neural network based facial recognition system[J].Processing of SPIF,1994,22(43):595-606
22.Lin S H,Kung S Y.Face recognition/detection by probabilistic decision based neural network[J].IEEE Trans on Neural Networks,1997.8(1):14-132
23.M.Lades,J.C.Vorbruggen.Distortion invariant object recognition in the dynamic link architecture[J].IEEE Trans.on Computer,1993,42(3):300-311
24.L.Wiskott.Recognizing faces by dynamic link matching[J].Neuroimage,1996,4(3):514-518
25.章毓晋.图像处理和分析(图像工程上册)[M].北京:清华大学出版社,2003:181-182
26.张宏林.Visual C++数字图像模式识别技术及工程实践[M].北京:人民邮电出版社,2003:363-421
27.Rein-Lien Hsu,Mohamed Abdel-Mottaleb.Face detection in color images[J].IEEE Trans.Pattern Anal.Mach.Intell,2002,24(5):696-706
28.J Kovac P Peer,F Solina.Human skin colour clustering for face detection[J].Faculty of computer and information science,2003,2(22-24):144-14.8
29.何斌,马天予,王运坚.Visual C++数字图像处理LM].北京:人民邮电出版社,2002:7-8
30.Sankur B,Sezgin M.A survey over image thresh holding techniques and quantitative performance evaluation[J].Journal of Electronic Imaging,2004,13(1):146-165
31.郑南宁.机器视觉与模式识别[M].北京:国防工业出版社,1998:66-69
32.朱夏君.人脸识别的研究及眼睛定位算法[D].杭州:浙江工业大学,2003
33.张杰,杨晓飞,赵瑞莲.基于Hough变换圆检测的人眼精确定位方法[J].计算机工程与应用,2005,27(2):43-44
34.孙亦南,刘伟军,王越超等.一种用于圆检测的改进Hough变换方法[J].计算机工程与应用,2003,39(20):35-37
35.孙家广.计算机图形学[M].清华大学出版社,2006:308-318
36.梁路宏,艾海舟,徐光祐,张钹.人脸检测研究综述[J].计算机学报,2002,25(5):449-456
37.Turk M,Pentland A P.Eigenfaces for recognition[J].Journal of Cognitive Neuroscience,1991,3(1):71-86
38.Rabiner L R.A tutorial on hidden Markov models and selected applications in speech recognition[J].Proceedings of the IEEE,1989,77(2):257-286
39.Nefian A.A hidden Markov model based approach for face detection and recognition[D].Georgia:Georgia Institute of Technology,1999
40.Eickeler S,M(u|¨)llerS,Rigoll GRecognition of JPEG compressed face images based on statistical methods[J].Image and Vision Computing,2000,18(4):279-287
41.Othman.A simplified second-order HMM with application to face recognition[J].IEEE International Symposium on Circuits and Systems,2001,2(6-9):161-164
42.Samaria F,Yong S.HMM-based architecture.for face identification[J].Image and Vision Computing,1994,12(8):537-543
43.Nefian A V.Hidden markov models for face recognition[J].Proceedings of the IEEE International Conference on Acoustics,Speech and Signal Processing,1998,5(1):2721-2724
44.张旭东.图像编码基础和小波压缩技术[M].北京:清华大学出版社,2004:165-208
45.徐毅琼等.基于隐马尔可夫模型的自动人脸识别方法[J].计算机应用.2004.24(12):227
46.李杰.隐马尔可夫模型的研究及其在图像识别中的应用[D].北京:清华大学,2004