基于轮廓线的三维人脸识别算法的研究
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摘要
人脸识别是基于生物特征的认证技术中具有挑战性的领域之一,也是本世纪有良好发展潜力的技术之一。作为自然而友好的身份识别方式,人脸识别已经成为模式识别和图像处理中的重要研究热点。
使用二维图像人脸识别方法,由于受到光照、姿势、表情变化的影响,其识别的准确度受到很大限制。迄今为止,建立一个鲁棒的人脸识别系统仍然是一个很困难的问题。由于3D数据本身具有显式的几何形状信息,因此3D人脸识别更具克服姿态和表情困难的潜力。本文主要针对三维人脸识别,在以下几个方面展开了研究工作:
1、在初始三维数点云数据量足够大的情况下,尝试使用B样条曲面拟合生成的网格控制顶点模拟三维点云数据。这个方法提高了点云数据的规格化程度,并大大减少了数据量,提高了算法效率。
2、确定了三维人脸坐标系,并结合深度信息特点提取轮廓线,进行了曲率计算和分析,进而提取鼻子距离特征和人脸中分轮廓线分段曲率特征用于识别。降维处理简化了算法复杂度。
3、分析特征向量的特性,利用欧式距离法和互相关函数进行样本间相似性度量,完成了人脸识别算法。
4、在理论研究的同时,我们采用ViusalC++6.0以及SQL数据库后台设计实现了实验性的三维人脸识别系统平台。该系统能提取人脸轮廓线,并进行曲率计算和分析,从中提取人脸特征向量组,通过欧式距离法和互相关函数相似度比较实现三维人脸识别。试验结果验证了算法的可行性。
Face recognition is one of the most active and challenging technologies. As a natural and friendly way, automatic face recognition has become an important part of the researches of image processing and pattern recognition.
Because of the influence of illumination, pose variation and expression, the improvement of recognition accuracy of 2D face recognition is greatly impeded and it is difficult to build a robust face recognition system. Due to its richer information contained for facial surface, the 3D face data has more promising potential to conquer the change of pose than 2D images.
This thesis addresses to study the 3D face recognition algorithms. The main contributions of the work are as follows:
1、If the data of initial 3-D point-cloud is enough , the grid control points can be used to simulate the point-cloud data which is created by B-spline surface fitting. We standardize the point-cloud data to reduce the quantity of point-cloud data to raise efficiency of our algorithm.
2、Contour lines are extracted according to depth information feature. Then, features of nose and the profile subsection curvature are obtained by analyzing curvatures of contour lines.
3、The characteristic of eigenvector is analyzed, and the similarity between face samples is measured using Euler distance and cross correlation function.
4、The 3D face recognition system is developed using visual C++6.0 and SQL Server 2000. The contour lines of the face are found and their curvatures are computed first. Then, by analyzing curvatures the eigenvectors of face are extracted. Finally, the similarities between face samples are measured to recognize the face. Experiments have been conducted to show the feasibility of our algorithm.
使用二维图像人脸识别方法,由于受到光照、姿势、表情变化的影响,其识别的准确度受到很大限制。迄今为止,建立一个鲁棒的人脸识别系统仍然是一个很困难的问题。由于3D数据本身具有显式的几何形状信息,因此3D人脸识别更具克服姿态和表情困难的潜力。本文主要针对三维人脸识别,在以下几个方面展开了研究工作:
1、在初始三维数点云数据量足够大的情况下,尝试使用B样条曲面拟合生成的网格控制顶点模拟三维点云数据。这个方法提高了点云数据的规格化程度,并大大减少了数据量,提高了算法效率。
2、确定了三维人脸坐标系,并结合深度信息特点提取轮廓线,进行了曲率计算和分析,进而提取鼻子距离特征和人脸中分轮廓线分段曲率特征用于识别。降维处理简化了算法复杂度。
3、分析特征向量的特性,利用欧式距离法和互相关函数进行样本间相似性度量,完成了人脸识别算法。
4、在理论研究的同时,我们采用ViusalC++6.0以及SQL数据库后台设计实现了实验性的三维人脸识别系统平台。该系统能提取人脸轮廓线,并进行曲率计算和分析,从中提取人脸特征向量组,通过欧式距离法和互相关函数相似度比较实现三维人脸识别。试验结果验证了算法的可行性。
Face recognition is one of the most active and challenging technologies. As a natural and friendly way, automatic face recognition has become an important part of the researches of image processing and pattern recognition.
Because of the influence of illumination, pose variation and expression, the improvement of recognition accuracy of 2D face recognition is greatly impeded and it is difficult to build a robust face recognition system. Due to its richer information contained for facial surface, the 3D face data has more promising potential to conquer the change of pose than 2D images.
This thesis addresses to study the 3D face recognition algorithms. The main contributions of the work are as follows:
1、If the data of initial 3-D point-cloud is enough , the grid control points can be used to simulate the point-cloud data which is created by B-spline surface fitting. We standardize the point-cloud data to reduce the quantity of point-cloud data to raise efficiency of our algorithm.
2、Contour lines are extracted according to depth information feature. Then, features of nose and the profile subsection curvature are obtained by analyzing curvatures of contour lines.
3、The characteristic of eigenvector is analyzed, and the similarity between face samples is measured using Euler distance and cross correlation function.
4、The 3D face recognition system is developed using visual C++6.0 and SQL Server 2000. The contour lines of the face are found and their curvatures are computed first. Then, by analyzing curvatures the eigenvectors of face are extracted. Finally, the similarities between face samples are measured to recognize the face. Experiments have been conducted to show the feasibility of our algorithm.
引文
[1]W.Zhao,R.Chellappa,A.Rosenfeld,P.J.Phillips,Face Recognition:A Literatrue Survey.ACM Computing Surveys,2003:p.399-458.
[2]潘刚.三维人脸识别若干技术研究.浙江大学博士毕业论文,2003.
[3]王跃明.表情不变的三维人脸识别研究.浙江大学博士毕业论文,2007.
[4]K.Chang,K.Bowyer,P.Flynn,Effects on Facial Expression in 3D Face Recognition.Proc.of the SPIE,2005.5779:p.132-143.
[5]K.Chang,K.Bowyer,P,Flynn.Adaptive rigid multi-region selection for handling expression variation in 3D face recognition,in IEEE Workshop on Face Recognition Grand Challenge Experiments.2005.
[6]D.P.Huttenlocher,G.A.Klanderman,W.J.Rucklidge,Comparing images using the hausdorff distance.IEEE Transactions on Pattern Analysis and Machine Intelligence,1993.15(9):p.850-863.
[7]A.E.Johnson,M.Hebert,Using spin images for ej}cient object recognition in cluttered 3D scenes.IEEE Trans.PAMI,1999.21(5):p.433-449.
[8]C.S.Chua,F.Han,YK.H.3D Human Face Recognition Using Point Signature.in Int'l Conf.on Automatic Face and Gesture Recognition.2000.
[9]刘晓宁.基于三维模型的人脸识别技术研究.西北大学博士毕业论文,2006.
[10]Y.Lee,H.Song,U.Yang,H.Shin,K.Sohn.Local feature based 3D face recognition,in International Conference on Audio- and }deobased Biometric Person Authentication.2005.
[11]Y.Lee,J.Shim.Curvature-based human face recognition using depth-weighted Hausdorff distance,in International Conference on Image Processing(ICIP).2004.
[12]J.C.Lee,E.Milios.Matching range images of human faces.In International Conference on Computer Vision.1990.
[13]G.Gordon.Face recognition based on depth and curvature features,in Computer Vision and Pattern Recognition(CVPR).1992.
[14]FRGC.http://www.frvt.org/FRGC/.
[15]H.T. Tanaka, M. Ikeda, H. Chiaki. Curvature-based face surface recognition using spherical correlation principal directions for curved object recognition. in Third International Conference on Automated Face and Gesture Recognition. 1998.
[16]C. Xu, Y. Wang, T. Tan, L. Quan. Automatic 3D face recognition combining global geometric features with local shape variation information. in Sixth International Conference on Automated Face and Gesture Recognition. 2004.
[17]G. Givens, R. Beveridge, B. Draper, D. Bolme. A statistical assessment of subject factors in the PCA recognition of human faces, in Workshop on Statistical Analysis in Computer Vision (in CVPR). 2003.
[18]P. N. Bellhumer, J. Hespanha, D. Kriegman, Eigenfaces vs. fisherfaces: Recognition using class specific linear projection. IEEE Transactions on Pattern Analysis and Machine Intelligence, Special Issue on Face Recognition, 1997. 17(7): p. 711-720.
[19]C. Hesher, A. Srivastava, G Erlebacher. A novel technique for face recognition using range imaging, in eventh International Symposium on Signal Processing and Its Applications. 2003.
[20]Y.M. Wang, G. Pan, Z. H. Wu, Y. G. Wang. Exploring Facial Expression Effects in 3D Face Recognition using Partial ICP. In The 7 th Asian Conference on Computer Vision (ACCV' 06). 2006.
[21]A. M. Bronstein, M. M.Bronstein, R. Kimmel, Three-dimensional face recognition. International Journal of Computer Vision, 2005.
[22]3D_RMA, http://www.sic.ram.ac.be/~～eumier/DB/3d_rma.html.
[23] 胡永利,尹宝才,程世铨,谷春亮,刘文韬. 创建中国人三维人脸库关键技术研究. 42 (4):622-628, 2005.
[24]BJUT. http://www.bjut,edu.cn/sci/sultimedia/mul-lab/3dface/overview.htm.
[25]X. Lu, A. K. Jain, D. Colbry, Matching 2.5D Face Scans to 3D Models. IEEE Transactions on Pattern Analysis and Machine Interrlgence (PAMI), 2006. 28(1): P. 31-43.
[26]X. Lu, A. K. Jain, D. Colbry. Deformation Analysis for 3D Face Matching. In Proc. 7th IEEE Workshop on Applications of Computer Vision(WACV'05).2005.
[27]A.S.Mian,M.Bennamoun,R.Owens.2D & 3D Multimodal Hybrid Face Recognition.in ECCV.2006.
[28]Y Wang,C.Chua,Y Ho,Facial feature detection and face recognition from 2D and 3D images.Pattern Recognition Letters,2002.23 p.1191-1202.
[29]T.Papatheodorou,D.Reuckert.Evaluation of automatic 4D face recognition using surface and texture registration,in Sixth International Conference on Automated Face and Gesture Recognition.2004.
[30]P.J.Phillips,P.J.Flynn,T.Scruggs,K.W.Bowyer,J.Chang,K.Hoffman,J.Marques,J.Min,W.Worek.Overview of the Face Recognition Grand Challenge.in Proc.of CYPR.2005.
[31]G.Passalis,I.Kakadiaris,T.Theoharis,G.Toderici,N.Murtuza.Evaluation of 3D face recognition in the presence of facial expressions:an annotated deformable model approach,in IEEE Workshop on Face Recognition Grand Challenge Experiments.2005.
[32]PRISM.http://prism.asu.edu/3dface/default.asp.
[33]Cyberware,http://www.cyberware.com.
[34]Minolta,http://minoltaeurope.com/3d/3d.html.
[35]Shapecapture,http://www.shapecature.com/.
[36]Triclops,http://www.ptgrey.com.
[37]P.J.Phillips,H.Moon,S.A.Rizvi,P.J.Rauss,The FEAET Evaluation Methodology for Face-Recognition Algorithms.IEEE Transactions on Pattern Analysis and Machine Intelligence,2000.00:p.1090-1104.
[38]PIE,Database,http://www.ri.cmu.edu/projects/project_418.html.2000.
[39]张晓华,山世光,曹波,高文,周德龙,赵德斌.CAS-PEAL大规模中国人脸图像数据库及其基本测评介绍.计算机辅助设计与图形学学报,2005.17(1):p.9-17.
[40]L.Yin,X.Wei,Y.Sun,J.Wang,M.Rosato.A 3D facial expression database for facial behavior research.In 7th International Conference on Automatic Face and Gesture Recognition(FG2006).2006.
[41]X.Lu,A.K.Jain,D.Colbry.Deformation Analysis for 3D Face Matching.In Proc. 7th IEEE Workshop on Applications of Computer Vision(WACV'05).2005.
[42]F.Tsalakanidou,S.Malasiotis,M.G.Strintzis,FaceLocalization and Authentication Using Color and Depth Images.IEEE Transactions on Image Processing,2005.14(2):p.152-168.
[43]C.Xu,T.Tan,S.Li,Y.Wang,C.Zhong.Learning Effective Intrinsic Features to Boost 3D-Based Face Recognition.In ECCV 2006.
[44]Gavab DB http://gavab.escet.urjc.es.
[45]G.Pan,Z.H.Wu,YH.Pan.Automatic 3D Face Verification From Range Data.in Proc.IEEE International Conference on Acoustics,Speech,and Signal Processing (ICASSP'03).2003.
[46]施法中,计算机辅助几何设计与非均匀有理B样条[M].北京:高等教育出版社,2001.
[47]C.J.Wu and J.S Huang.Human face profile recognition by computer.Pattern Recognition,vol.23,no.3/4,pp.255-259,1990.
[48]J.C.Campos,A.D.Liney,and J.P.Moss.The analysis of facial profiles using scale space techniques.Pattern Recognition,vol.26,pp.819-824,1993.
[49]Behzad Dariush,Sing Bing Kang,and Keith Waters.Spatiotemporal analysis of face profiles:detection,segmentation,and registration.In Proceedings of IEEE International Conference on Automatic Face and Gestrue Recognition,pages 248-253,1998.
[50]吴大任 编.微分几何讲义,人民教育出版社,北京,1982.
[51]王学松.三维人面貌特征提取与识别技术研究.西北大学硕士毕业论文,2003.
[52]章毓晋.图像工程(下册)-图像理解与计算机视觉[M].北京:清华大学出版社,2000.
[53]彭华.基于线面特征的双目立体视觉研究与应用[D].厦门:厦门大学,2007.
[54]曾弘博.框架特征下双目立体视觉中的三维重建研究[D].厦门大学,2007
[55]李王伟.基于内容的交通视频检索系统算法研究[D].厦门:厦门大学,2006
[56]吴绿芳.立体视觉中人脸的三维数据获取研究[D].厦门:厦门大学,2008.
[57]吴众山.基于B样条的三维人脸曲面生成及特征提取研究[D].厦门大学,2008
[2]潘刚.三维人脸识别若干技术研究.浙江大学博士毕业论文,2003.
[3]王跃明.表情不变的三维人脸识别研究.浙江大学博士毕业论文,2007.
[4]K.Chang,K.Bowyer,P.Flynn,Effects on Facial Expression in 3D Face Recognition.Proc.of the SPIE,2005.5779:p.132-143.
[5]K.Chang,K.Bowyer,P,Flynn.Adaptive rigid multi-region selection for handling expression variation in 3D face recognition,in IEEE Workshop on Face Recognition Grand Challenge Experiments.2005.
[6]D.P.Huttenlocher,G.A.Klanderman,W.J.Rucklidge,Comparing images using the hausdorff distance.IEEE Transactions on Pattern Analysis and Machine Intelligence,1993.15(9):p.850-863.
[7]A.E.Johnson,M.Hebert,Using spin images for ej}cient object recognition in cluttered 3D scenes.IEEE Trans.PAMI,1999.21(5):p.433-449.
[8]C.S.Chua,F.Han,YK.H.3D Human Face Recognition Using Point Signature.in Int'l Conf.on Automatic Face and Gesture Recognition.2000.
[9]刘晓宁.基于三维模型的人脸识别技术研究.西北大学博士毕业论文,2006.
[10]Y.Lee,H.Song,U.Yang,H.Shin,K.Sohn.Local feature based 3D face recognition,in International Conference on Audio- and }deobased Biometric Person Authentication.2005.
[11]Y.Lee,J.Shim.Curvature-based human face recognition using depth-weighted Hausdorff distance,in International Conference on Image Processing(ICIP).2004.
[12]J.C.Lee,E.Milios.Matching range images of human faces.In International Conference on Computer Vision.1990.
[13]G.Gordon.Face recognition based on depth and curvature features,in Computer Vision and Pattern Recognition(CVPR).1992.
[14]FRGC.http://www.frvt.org/FRGC/.
[15]H.T. Tanaka, M. Ikeda, H. Chiaki. Curvature-based face surface recognition using spherical correlation principal directions for curved object recognition. in Third International Conference on Automated Face and Gesture Recognition. 1998.
[16]C. Xu, Y. Wang, T. Tan, L. Quan. Automatic 3D face recognition combining global geometric features with local shape variation information. in Sixth International Conference on Automated Face and Gesture Recognition. 2004.
[17]G. Givens, R. Beveridge, B. Draper, D. Bolme. A statistical assessment of subject factors in the PCA recognition of human faces, in Workshop on Statistical Analysis in Computer Vision (in CVPR). 2003.
[18]P. N. Bellhumer, J. Hespanha, D. Kriegman, Eigenfaces vs. fisherfaces: Recognition using class specific linear projection. IEEE Transactions on Pattern Analysis and Machine Intelligence, Special Issue on Face Recognition, 1997. 17(7): p. 711-720.
[19]C. Hesher, A. Srivastava, G Erlebacher. A novel technique for face recognition using range imaging, in eventh International Symposium on Signal Processing and Its Applications. 2003.
[20]Y.M. Wang, G. Pan, Z. H. Wu, Y. G. Wang. Exploring Facial Expression Effects in 3D Face Recognition using Partial ICP. In The 7 th Asian Conference on Computer Vision (ACCV' 06). 2006.
[21]A. M. Bronstein, M. M.Bronstein, R. Kimmel, Three-dimensional face recognition. International Journal of Computer Vision, 2005.
[22]3D_RMA, http://www.sic.ram.ac.be/~～eumier/DB/3d_rma.html.
[23] 胡永利,尹宝才,程世铨,谷春亮,刘文韬. 创建中国人三维人脸库关键技术研究. 42 (4):622-628, 2005.
[24]BJUT. http://www.bjut,edu.cn/sci/sultimedia/mul-lab/3dface/overview.htm.
[25]X. Lu, A. K. Jain, D. Colbry, Matching 2.5D Face Scans to 3D Models. IEEE Transactions on Pattern Analysis and Machine Interrlgence (PAMI), 2006. 28(1): P. 31-43.
[26]X. Lu, A. K. Jain, D. Colbry. Deformation Analysis for 3D Face Matching. In Proc. 7th IEEE Workshop on Applications of Computer Vision(WACV'05).2005.
[27]A.S.Mian,M.Bennamoun,R.Owens.2D & 3D Multimodal Hybrid Face Recognition.in ECCV.2006.
[28]Y Wang,C.Chua,Y Ho,Facial feature detection and face recognition from 2D and 3D images.Pattern Recognition Letters,2002.23 p.1191-1202.
[29]T.Papatheodorou,D.Reuckert.Evaluation of automatic 4D face recognition using surface and texture registration,in Sixth International Conference on Automated Face and Gesture Recognition.2004.
[30]P.J.Phillips,P.J.Flynn,T.Scruggs,K.W.Bowyer,J.Chang,K.Hoffman,J.Marques,J.Min,W.Worek.Overview of the Face Recognition Grand Challenge.in Proc.of CYPR.2005.
[31]G.Passalis,I.Kakadiaris,T.Theoharis,G.Toderici,N.Murtuza.Evaluation of 3D face recognition in the presence of facial expressions:an annotated deformable model approach,in IEEE Workshop on Face Recognition Grand Challenge Experiments.2005.
[32]PRISM.http://prism.asu.edu/3dface/default.asp.
[33]Cyberware,http://www.cyberware.com.
[34]Minolta,http://minoltaeurope.com/3d/3d.html.
[35]Shapecapture,http://www.shapecature.com/.
[36]Triclops,http://www.ptgrey.com.
[37]P.J.Phillips,H.Moon,S.A.Rizvi,P.J.Rauss,The FEAET Evaluation Methodology for Face-Recognition Algorithms.IEEE Transactions on Pattern Analysis and Machine Intelligence,2000.00:p.1090-1104.
[38]PIE,Database,http://www.ri.cmu.edu/projects/project_418.html.2000.
[39]张晓华,山世光,曹波,高文,周德龙,赵德斌.CAS-PEAL大规模中国人脸图像数据库及其基本测评介绍.计算机辅助设计与图形学学报,2005.17(1):p.9-17.
[40]L.Yin,X.Wei,Y.Sun,J.Wang,M.Rosato.A 3D facial expression database for facial behavior research.In 7th International Conference on Automatic Face and Gesture Recognition(FG2006).2006.
[41]X.Lu,A.K.Jain,D.Colbry.Deformation Analysis for 3D Face Matching.In Proc. 7th IEEE Workshop on Applications of Computer Vision(WACV'05).2005.
[42]F.Tsalakanidou,S.Malasiotis,M.G.Strintzis,FaceLocalization and Authentication Using Color and Depth Images.IEEE Transactions on Image Processing,2005.14(2):p.152-168.
[43]C.Xu,T.Tan,S.Li,Y.Wang,C.Zhong.Learning Effective Intrinsic Features to Boost 3D-Based Face Recognition.In ECCV 2006.
[44]Gavab DB http://gavab.escet.urjc.es.
[45]G.Pan,Z.H.Wu,YH.Pan.Automatic 3D Face Verification From Range Data.in Proc.IEEE International Conference on Acoustics,Speech,and Signal Processing (ICASSP'03).2003.
[46]施法中,计算机辅助几何设计与非均匀有理B样条[M].北京:高等教育出版社,2001.
[47]C.J.Wu and J.S Huang.Human face profile recognition by computer.Pattern Recognition,vol.23,no.3/4,pp.255-259,1990.
[48]J.C.Campos,A.D.Liney,and J.P.Moss.The analysis of facial profiles using scale space techniques.Pattern Recognition,vol.26,pp.819-824,1993.
[49]Behzad Dariush,Sing Bing Kang,and Keith Waters.Spatiotemporal analysis of face profiles:detection,segmentation,and registration.In Proceedings of IEEE International Conference on Automatic Face and Gestrue Recognition,pages 248-253,1998.
[50]吴大任 编.微分几何讲义,人民教育出版社,北京,1982.
[51]王学松.三维人面貌特征提取与识别技术研究.西北大学硕士毕业论文,2003.
[52]章毓晋.图像工程(下册)-图像理解与计算机视觉[M].北京:清华大学出版社,2000.
[53]彭华.基于线面特征的双目立体视觉研究与应用[D].厦门:厦门大学,2007.
[54]曾弘博.框架特征下双目立体视觉中的三维重建研究[D].厦门大学,2007
[55]李王伟.基于内容的交通视频检索系统算法研究[D].厦门:厦门大学,2006
[56]吴绿芳.立体视觉中人脸的三维数据获取研究[D].厦门:厦门大学,2008.
[57]吴众山.基于B样条的三维人脸曲面生成及特征提取研究[D].厦门大学,2008