基于统计学习的人脸图像合成方法研究
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摘要
人脸图像合成是新一代人机交互中的重要技术,也是当前活跃的研究方向,在计算机图形学和计算机视觉界都得到广泛的关注。其潜在的应用领域包括窄带视频传输、计算机辅助教学、游戏制作、虚拟现实等等。
传统方法采用精细的网格模型或曲面模型建立人头三维结构,采用生理模型或参数模型进行人脸动画。这类方法在三维数据获取设备的易用性、模型表示的准确性、算法复杂度及鲁棒性等方面还存在不少问题和矛盾。近年发展起来的基于样本方法可以不做三维重构而直接利用样本图像合成人脸。这类方法不需三维建模,避免了建模误差,其合成结果具有与样本图像相似的真实感。
本文正是着眼于基于图像样本的人脸合成方法,探讨在完全不提取三维信息的情况下,如何利用图像样本集和统计学习理论,合成大角度的人脸姿态变化,模拟不同的人脸光照效果,以及为任意人生成真实感表情图像。本文在充分回顾已有方法的基础上,在人脸合成问题的不同方面提出了若干创新性想法,并给出让人振奋的实验结果。本文的工作显示了基于统计学习方法进行人脸合成的特点和优势,为人脸建模和动画技术的发展开拓了新思路。
本文的主要研究内容和创新点包括以下方面。
第一,提出了一种基于因素分解模型的多姿态人脸图像合成方法。这里将“人的身份”和“头部姿态”看作影响人脸图像的两种变化因素,用多姿态人脸图像数据库做为训练集学习这两种因素的交互作用。当给出一张测试人脸图像时,利用因素分解模型的“转移”功能,就可以生成测试人脸在训练集已有姿态下的图像和训练集人脸在测试人脸姿态下的图像。这里还应用核函数方法将线性因素分解模型扩展到非线性情况,有效地解决了原模型在“转移”应用中的局部极小问题。对于训练集外的任意人脸,经过“光照校正”的预处理和“图像变形”的后处理,就能合成出这张人脸在不同姿态下的图像。这里提出的方法可以用于多姿态人脸数据库的构建、多姿态人脸的识别和验证等等。
第二,提出了“形状纹理关联映射”的思想,用于解决人脸动态特征的真实感表现问题。关联映射的基本思想是说在图像中人脸特征的形状和纹理有一定的关联关系,如果找到了从形状到纹理的这种关系并以映射的数学形式进行
表达,就可以根据人脸的形状变化自动地生成真实自然的动态纹理。这里以脸部变化最复杂的部分——嘴部图像——的合成为例,给出了关联映射的具体实现方法。利用这个映射可以使用很少的几个形状参数重构出整个嘴部图像的变化。另外,在一段人脸表情视频上实现的关联映射表明,仅由人脸特征点位置偏移就能相当成功地恢复出脸部表情的变化细节。这里提出的技术可以集成到会说话的人头系统中生成真实感嘴部动画,也可以应用到基于模型的视频编码系统中来进一步节省传输带宽。
第三,提出了一种情感参数控制的真实感表情图像生成方法。借助于一个人脸表情图像数据库,可以训练出从情感状态到表情图像的映射,这里称作“情感函数”。情感函数能够以任意人的中性脸为输入,根据情感参数的控制,为这个人生成相应的情感图像。情感函数实际上描述了脸部表情随内心情感的变化方式,因此可以用不同的数据集训练不同类型的情感函数,以表现出不同风格的情感。在建立人脸表情图像的参数化统计模型时,为使合成算法适用于任意人,模型训练采用了相对量而不是绝对量。以每个人的中性脸为参考点,将表情脸与中性脸做减法得到形状相对量,做除法得到纹理相对量。这样能够以独立于特定人的方式提取出表情变化量。从实验结果看,这里提出的方法仅用一张中性脸图像,就可以为任意人合成情感状态可控的真实感表情图像。
Realistic face image synthesis appears as an important technique in the field of human computer interaction. It is also an active research topic both in computer vision and computer graphics community. The potential application of this technique includes low bit-rate video transmission, computer aided instruction, game design, virtual reality and so on.
Traditional approaches use wire-frame model or surface model to build 3D head structure, and use physiological model or parametric model to make facial animation. However, there are many problems to be solved with it on the acquisition of 3D data, the accuracy of model representation, the complexity and the robustness of algorithms. Most recently, the example-based approaches are also actively adopted in face synthesis. This new strategy, utilizing example images directly, without any 3D reconstruction, can often achieve more realistic effects than traditional methods.
This thesis focuses on example-based face synthesis, discusses how to utilize training examples and statistic methods to synthesize face image in a wide range of view, under different lighting condition, and with various emotional expressions. We summarize the relevant literatures first, and then propose our novel ideas and show the exciting results by interesting experiments. Our work demonstrates the advantage of statistical learning theory in solving problem of face image synthesis. It also points out a new direction of development in the field of face modeling and animation.
The main contributions of this work are listed as follows.
First, a multi-view face synthesis method based on factorization model is proposed. Here “human identity” and “head pose” are regarded as two influence factors, and their interaction is trained with a face database. With the special ability provided by factorization model, a test face can be translated into old views contained in training faces, and training faces can also be translated into new view of the test face. The original bilinear factorization model is also extended to nonlinear case so
that global optimum solution can be found in solving “translation” task. Thus, with a pre-processing and a post-processing procedure, an arbitrary new face is able to be translated into other views. The proposed method can be applied to areas such as multi-view face database building and face recognition across a wide range of view.
Second, a method of dynamic facial texture generation based on shape appearance dependence mapping is proposed. It has been proved that there is a high correlation between shape and texture of facial features. Based on this observation, the dynamic facial texture can be generated according to shape variation and this strategy is called dependence mapping. We implement the dependence mapping on mouth region and show that a realistic mouth animation can be generated according to several shape parameters. We also test performance of the mapping by using a video clip of facial expression. The experiment shows that the expressive details are successfully recovered from the movement of facial feature points. The proposed technique can be integrated to a talking head system to generate realistic animation, or applied to a model-based coding system to produce more efficient bit-rates.
Third, a simple methodology for mimicking realistic face by manipulating emotional status is proposed. A mapping from emotional status to facial expression is trained with a face database. The mapping is called “emotional function” which can be used to generate expressive face for new person by utilizing just his/her neutral image. In fact the emotional function describes the way of expression variation according to inner emotion. Because of this, the function can be trained with different training set to reflect different affective style. While building the statistical model for face image, the model is trained in relative way rather than in original way. This training strategy makes expressive details extracted in person independent manner. As the experi
传统方法采用精细的网格模型或曲面模型建立人头三维结构,采用生理模型或参数模型进行人脸动画。这类方法在三维数据获取设备的易用性、模型表示的准确性、算法复杂度及鲁棒性等方面还存在不少问题和矛盾。近年发展起来的基于样本方法可以不做三维重构而直接利用样本图像合成人脸。这类方法不需三维建模,避免了建模误差,其合成结果具有与样本图像相似的真实感。
本文正是着眼于基于图像样本的人脸合成方法,探讨在完全不提取三维信息的情况下,如何利用图像样本集和统计学习理论,合成大角度的人脸姿态变化,模拟不同的人脸光照效果,以及为任意人生成真实感表情图像。本文在充分回顾已有方法的基础上,在人脸合成问题的不同方面提出了若干创新性想法,并给出让人振奋的实验结果。本文的工作显示了基于统计学习方法进行人脸合成的特点和优势,为人脸建模和动画技术的发展开拓了新思路。
本文的主要研究内容和创新点包括以下方面。
第一,提出了一种基于因素分解模型的多姿态人脸图像合成方法。这里将“人的身份”和“头部姿态”看作影响人脸图像的两种变化因素,用多姿态人脸图像数据库做为训练集学习这两种因素的交互作用。当给出一张测试人脸图像时,利用因素分解模型的“转移”功能,就可以生成测试人脸在训练集已有姿态下的图像和训练集人脸在测试人脸姿态下的图像。这里还应用核函数方法将线性因素分解模型扩展到非线性情况,有效地解决了原模型在“转移”应用中的局部极小问题。对于训练集外的任意人脸,经过“光照校正”的预处理和“图像变形”的后处理,就能合成出这张人脸在不同姿态下的图像。这里提出的方法可以用于多姿态人脸数据库的构建、多姿态人脸的识别和验证等等。
第二,提出了“形状纹理关联映射”的思想,用于解决人脸动态特征的真实感表现问题。关联映射的基本思想是说在图像中人脸特征的形状和纹理有一定的关联关系,如果找到了从形状到纹理的这种关系并以映射的数学形式进行
表达,就可以根据人脸的形状变化自动地生成真实自然的动态纹理。这里以脸部变化最复杂的部分——嘴部图像——的合成为例,给出了关联映射的具体实现方法。利用这个映射可以使用很少的几个形状参数重构出整个嘴部图像的变化。另外,在一段人脸表情视频上实现的关联映射表明,仅由人脸特征点位置偏移就能相当成功地恢复出脸部表情的变化细节。这里提出的技术可以集成到会说话的人头系统中生成真实感嘴部动画,也可以应用到基于模型的视频编码系统中来进一步节省传输带宽。
第三,提出了一种情感参数控制的真实感表情图像生成方法。借助于一个人脸表情图像数据库,可以训练出从情感状态到表情图像的映射,这里称作“情感函数”。情感函数能够以任意人的中性脸为输入,根据情感参数的控制,为这个人生成相应的情感图像。情感函数实际上描述了脸部表情随内心情感的变化方式,因此可以用不同的数据集训练不同类型的情感函数,以表现出不同风格的情感。在建立人脸表情图像的参数化统计模型时,为使合成算法适用于任意人,模型训练采用了相对量而不是绝对量。以每个人的中性脸为参考点,将表情脸与中性脸做减法得到形状相对量,做除法得到纹理相对量。这样能够以独立于特定人的方式提取出表情变化量。从实验结果看,这里提出的方法仅用一张中性脸图像,就可以为任意人合成情感状态可控的真实感表情图像。
Realistic face image synthesis appears as an important technique in the field of human computer interaction. It is also an active research topic both in computer vision and computer graphics community. The potential application of this technique includes low bit-rate video transmission, computer aided instruction, game design, virtual reality and so on.
Traditional approaches use wire-frame model or surface model to build 3D head structure, and use physiological model or parametric model to make facial animation. However, there are many problems to be solved with it on the acquisition of 3D data, the accuracy of model representation, the complexity and the robustness of algorithms. Most recently, the example-based approaches are also actively adopted in face synthesis. This new strategy, utilizing example images directly, without any 3D reconstruction, can often achieve more realistic effects than traditional methods.
This thesis focuses on example-based face synthesis, discusses how to utilize training examples and statistic methods to synthesize face image in a wide range of view, under different lighting condition, and with various emotional expressions. We summarize the relevant literatures first, and then propose our novel ideas and show the exciting results by interesting experiments. Our work demonstrates the advantage of statistical learning theory in solving problem of face image synthesis. It also points out a new direction of development in the field of face modeling and animation.
The main contributions of this work are listed as follows.
First, a multi-view face synthesis method based on factorization model is proposed. Here “human identity” and “head pose” are regarded as two influence factors, and their interaction is trained with a face database. With the special ability provided by factorization model, a test face can be translated into old views contained in training faces, and training faces can also be translated into new view of the test face. The original bilinear factorization model is also extended to nonlinear case so
that global optimum solution can be found in solving “translation” task. Thus, with a pre-processing and a post-processing procedure, an arbitrary new face is able to be translated into other views. The proposed method can be applied to areas such as multi-view face database building and face recognition across a wide range of view.
Second, a method of dynamic facial texture generation based on shape appearance dependence mapping is proposed. It has been proved that there is a high correlation between shape and texture of facial features. Based on this observation, the dynamic facial texture can be generated according to shape variation and this strategy is called dependence mapping. We implement the dependence mapping on mouth region and show that a realistic mouth animation can be generated according to several shape parameters. We also test performance of the mapping by using a video clip of facial expression. The experiment shows that the expressive details are successfully recovered from the movement of facial feature points. The proposed technique can be integrated to a talking head system to generate realistic animation, or applied to a model-based coding system to produce more efficient bit-rates.
Third, a simple methodology for mimicking realistic face by manipulating emotional status is proposed. A mapping from emotional status to facial expression is trained with a face database. The mapping is called “emotional function” which can be used to generate expressive face for new person by utilizing just his/her neutral image. In fact the emotional function describes the way of expression variation according to inner emotion. Because of this, the function can be trained with different training set to reflect different affective style. While building the statistical model for face image, the model is trained in relative way rather than in original way. This training strategy makes expressive details extracted in person independent manner. As the experi
引文
Mehrabian A. Communication without words. Psychology Today, 1968, 2(4): 53-56
IBM research, http://www.research.ibm.com/natural/dreamspace/index.html
Microsoft Research, http://www.research.microsoft.com/easyliving/
Romdhani S, Blanz V, Vetter T. Face identification by fitting a 3D morphable model using linear shape and texture error functions. Computer Vision - ECCV 2002, LNCS 2353, 2002: 3-19
Yin L, Basu A. Generating realistic facial expressions with wrinkles for model-based coding. Computer Vision and Image Understanding, 2001, 84(2): 201-240
Abrantes G, Pereira F. MPEG-4 facial animation technology: survey, implementation and results. IEEE Transactions on Circuits and Systems for Video Technology, Special Issue on SNHC, 1999, 9(2) 290-305
MPEG Video, Information technology – Coding of audio-visual objects – Part 2: Visual, Amendment 1: Visual extensions, ISO/IEC JTC 1/SC 29/ WG 11/N3056, January, 2000
Ezzat T, Poggio T. Facial Analysis and synthesis using image-based models. Proc. of the Second International Conference on Automatic Face and Gesture Recognition, 1996: 116-121
Cosatto E, Graf H P. Sample-based synthesis of photo-realistic talking heads. Computer Animation, 1998: 103-110
Bregler C, Covell M, Slaney M. Video rewrite: driving visual speech with audio. Proc. of the ACM SIGGRAPH Conference on Computer Graphics, 1997: 353-360
Parke F I. Computer generated animation of faces. Proc. of the ACM National Conference, 1972: 451-457
Lee Y C, Terzopoulos D, Waters K. Constructing physics-based facial models of individuals. Proc. Graphics Interface, 1993: 1–8
Hong P, Wen Z, Huang T S. iFACE: A 3D synthetic talking face. International Journal of Image and Graphics, 2001, 1(1): 19-26
Blanz V, Vetter T. A morphable model for the synthesis of 3d faces. Proc. of the ACM SIGGRAPH Conference on Computer Graphics, 1999: 187–194
Fujiwara T, Koshimizu H, Fujimura K, Fujita G, Noguchi Y, Ishikawa N. 3D modeling system of human face and full 3D facial caricaturing. Proc. Seventh International Conference on Virtual Systems and Multimedia, 2001: 625–633
Guenter B, Grimm C, Wood D, Malvar D, Pighin F. Making Faces. Proc. of the ACM SIGGRAPH Conference on Computer Graphics, 1998: 55-66
Garcia E, Dugelay J L. Low cost 3D face acquisition and modeling. Proc. International Conference on Information Technology: Coding and Computing, 2001: 657–661
Akimoto T, Suenaga Y, Wallace R S. Automatic creation of 3D facial models. IEEE Computer Graphics and Applications, 1993, 13(3): 16-22
Lee W S, Escher M, Sannier G, Magnenat-Thalmann N. MPEG-4 compatible faces from orthogonal photos. Computer Animation, Conference Proceedings, 1999: 186-194
Goto T, Kshirsagar S, Magnenat-Thalmann N. Automatic face cloning and animation using real-time facial feature tracking and speech acquisition. IEEE Signal Processing Magazine, 2001, 18(3): 17–25
Lee W S, Magnenat-Thalmann N. Fast head modeling for animation. Image and Vision Computing, 2000, 18(4): 355-364
Fua P. Regularized bundle-adjustment to model heads from image sequences without calibration data. International Journal of Computer Vision, 2000, 38(2): 153-171
Shan Y, Liu Z, Zhang Z. Model-based bundle adjustment with application to face modeling. Proc. of International Conference on Computer Vision, 2001, 2: 644-651
Parke F I. A parametric model for human faces. Ph.D. Thesis, University of Utah, Salt Lake City, Utah, UTEC-CSc-75-047, 1974
Ekman P, Friesen W. Manual for the Facial Action Coding System. Consulting Psychologists Press, Palo Alto, 1986
Cohen M, Massara D. Modeling co-articulation in synthetic visual speech. In N. Magnenat Thalmann, D. Thalmann (eds.), Model and Technique in Computer Animation, 1993: 139-156
Haratsch E, Ostermann J. Parameter based animation of arbitrary 3D head models. Proc. of Picture Coding Symposium 1997, VDE Verlag, Berlin, Germany, September 1997: 81-84
Szijártó G, Kiss B, Takács B. Model-based real-time facial animation: design & implementation. In Conf. on Computer Animation & Geometric Modeling, 2002: 30-37
Rydfalk M. CANDIDE, a parameterized face. Report No. LiTH-ISY-I-866, Dept. of Electrical Engineering, Link?ping University, Sweden, 1987
Ahlberg J. CANDIDE-3 -- an updated parameterized face. Report No. LiTH-ISY-R-2326, Dept. of Electrical Engineering, Link?ping University, Sweden, 2001
Platt S, Badler N I. Animating facial expresiion. Computer Graphics, 1981, 15(3): 245-252
Waters K. A muscle model for animating three-dimensional facial expression. Computer Graphics, 1987, 21(4): 17-23
Magnenat-Thalmann N, Primeau E, Thalmann D. Abstract muscle action procedures for human face animation. Visual Computer, 1988, 3(5): 290-297
Terzopoulos D, Waters K. Analysis and synthesis of facial image sequences using physical and anatomical models. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1993, 15(6): 569-579
Lee Y, Terzopoulos D, Waters K. Realistic modeling for facial animation. Proc. of the ACM SIGGRAPH Conference on Computer Graphics, 1995: 55-62
Uz B, Gudukbay U, Ozguc B. Realistic speech animation of synthetic faces. Proc. Computer Animation, 1998: 111~118
Rao R R, Chen T. Audio-to-visual conversion for multimedia communication. IEEE Transactions on Industrial Electronics, 1998, 45(1): 15-22
Perng W L, Wu Y, Ouhyoung M. Image talk: a real time synthetic talking head using one single image with Chinese text-to-speech capability. Sixth Pacific Conference on Computer Graphics and Applications, 1998: 140-148
Arad N, Dyn N, Reisfeld D, Yeshurun Y. Image warping by radial basis functions: application to facial expressions. CVGIP: Graphical Models and Image Processing, 1994, 56(2): 161-172
Zhang Q, Liu Z, Guo B, Shum H. Geometry-driven photorealistic facial expression synthesis. Proc. ACM Symp. on Computer Animation 2003: 16-22
Beier T, Neely S. Feature-based image metamorphosis. Proc. of the ACM SIGGRAPH Conference on Computer Graphics, 1992: 35–42
Benson P J. Morph transformation of the facial image. Image and Vision Computing, 1994, 12(10): 691-696
Pighin F, Hecker J, Lischinski D, Szeliski R, Salesin D H. Synthesizing realistic facial expressions from photographs. Proc. of the ACM SIGGRAPH Conference on Computer Graphics, 1998: 75-84
Seitz S M, Dyer C R. View morphing. Proc. of the ACM SIGGRAPH Conference on Computer Graphics, 1996: 21-42
Shashua A, Riklin-Raviv T. Quotient image: class-based re-rendering and recognition with varying illuminations. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2001, 23(2): 129-139
Stoschek A. Image-based re-rendering of faces for continuous pose and illumination directions. Proc. of the IEEE Conference on Computer Vision and Pattern Recognition, 2000: 582–587
Wen Z, Liu Z, Huang T S. Face relighting with radiance environment maps. Proc. of the IEEE Conference on Computer Vision and Pattern Recognition, 2003: 158-165
Liu Z, Shan Y, Zhang Z. Expressive expression mapping with ratio images. Proc. of the ACM SIGGRAPH Conference on Computer Graphics, 2001: 271-276
Brand M E. Voice Puppetry. Proc. of the ACM SIGGRAPH Conference on Computer Graphics, 1999: 21-28
Yong Noh J, Neumann U. Expression cloning. Proc. of the ACM SIGGRAPH Conference on Computer Graphics, 2001: 277–288
Moghaddam B, Pentland A P. Probabilistic visual learning for object representation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1997, 19(7): 696–710
Turk M, Pentland A. Eigen faces for recognition. Journal of Cognitive Neuroscience, 1991, 3(1): 71~86
Beymer D. Vectorizing face images by interleaving shape and texture computations. M.I.T., A.I. Memo, No. 1537, 1995
Troje N F, Vetter T. Representations of human faces. Max-Planck-Institut fur biologische Kybernetik, Tubingen. MPI-memo No. 41, 1996
Vetter T, Poggio T. Linear object classes and image synthesis from a single example image. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1997, 19(7): 733-742
Vetter T. Synthesis of novel views from a single face image. International Journal of Computer Vision, 1998, 28(2): 103-116
Lanitis A, Taylor C J, Cootes T F. Automatic interpretation and coding of face images using flexible models. IEEE Transactions on Pattern Analysis and Machine Intelligence 1997, 19(7): 743-756
Romdhani S, Vetter T. Efficient, Robust and Accurate Fitting of a 3D Morphable Model. Proc. International Conference on Computer Vision, 2003, 2: 59-66
Cooper D H, Cootes T F, Taylor C J, Graham J. Active shape models - their training and application. Computer Vision and Image Understanding, 1995, 61(1): 38-59
Cootes T F, Taylor C J, Lanitis A. Multi-resolution search using active shape models. Proc. 12th International Conference on Pattern Recognition, 1994, 1: 610-612
Sim T, Baker S, Bsat M. The CMU pose, illumination, expression database. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2003, 25(12): 1615-1618.
Kanade T, Cohn J F, Tian Y. Comprehensive database for facial expression analysis. Proc. of the Fourth IEEE International Conference on Automatic Face and Gesture Recognition, 2000: 46-53
Lyons M J, Budynek J, Akamatsu S. Automatic classification of single facial images. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1999, 21(12): 1357-1362
Georghiades A S, Belhumeur P N, Kriegman D J. From few to many: illumination cone models for face recognition under variable lighting and pose. IEEE Transactions Pattern Analysis and Machine Intelligence, 2001, 23(6): 643-660
Phillips P J, Moon H, Rauss P J, Rizvi S. The FERET evaluation methodology for face recognition algorithms. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2000, 22(10): 1090-1104
Loui A C, Judice C N, Liu S. An image database for benchmarking of automatic face detection and recognition algorithms. Proc. of International Conference Image Processing, 1998: 146~150
Graham D B, Allinson N M. Characterizing virtual eigensignatures for general purpose face recognition. Face Recognition: From Theory to Applications, Sprigerverlag, 1998, 163: 446~456
The AR Face Database, http://rvl1.ecn.purdue.edu/~aleix/aleix_face_DB.html
CVL Face Database, http://www.lrv.fri.uni-lj.si/facedb.html
XM2VTSDB, http://xm2vtsdb.ee.surrey.ac.uk/home.html
Mukaigawa Y, Nakamura Y, Ohta Y. Synthesis of arbitrarily oriented face views from two images. Proc. of Asian Conference on Computer Vision, 1995, 3: 718-722
Ullman S, Basri R. Recognition by linear combinations of models. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1991, 13(10): 992-1006
Mukaigawa Y, Nakamura Y, Ohta Y. Facial animation from several images. Proc. of International Symposium on Real-Time Imaging and Dynamic Analysis, 1998, II(5), 906-911
Cootes T F, Wheeler G V, Walker K N, Taylor C J. View-based active appearance models. Image and Vision Computing, 2002, 20(9-10): 657-664
Okada K, Akamatsu S, Von der Malsburg C. Analysis and synthesis of pose variations of human faces by a linear PCMAP Model and its application for pose-invariant face recognition system. Proc. of Fourth International Conference on Automatic Face and Gesture Recognition, 2000: 142-149
Okada K, Von der Malsburg C. Analysis and synthesis of human faces with pose variations by a parametric piecewise linear subspace method. Proc. of the IEEE Conference on Computer Vision and Pattern Recognition, 2001, 1: I761-I768
Romdhani S, Gong S, Psarrou A. A multi-view non-linear active shape model using kernel PCA. Proc. 10-th British Machine Vision Conference, 1999, 2: 483-492
Edelman S, Weinshall D, Yeshurun Y. Learning to recognize faces from examples. Proc. of the 2nd European Conference on Computer Vision, 1992: 787–791
Lando M, Edelman S. Generalization from a single view in face recognition. Proc. of the International Workshop on Automatic Face and Gesture Recognition, 1995: 80-85
D'Zmura M, Iverson G. Color constancy: I. Basic theory of two-stage linear recovery of spectral descriptions for lights and surfaces. Journal of the Optical Society of America A: Optics and Image Science, 1993, 10(10): 2148-2165
Tomasi C, Kanade T. Shape and motion from image streams under orthography: a factorization method. International Journal of Computer Vision, 1992, 9(2): 137-154
Tenenbaum J B, Freeman W T. Separating style and content with bilinear models. Neural Computation, 2000, 12: 1247-1283
Chuang E, Deshpande H, Bregler C. Facial expression space learning. Proc. of Pacific Graphics, 2002: 68-76
Grimes D B, Shon A P, Rao R P N. Probabilistic bilinear models for appearance- based vision. Proc. of the IEEE International Conference on Computer Vision, 2003, 2: 1478-1485
Du Y, Lin X. Nonlinear factorization models using kernel approaches. Proc. Asian Conference on Computer Vision, 2004, 1: 426-431
Davis J W, Gao H. Recognizing human action efforts: An adaptive three-mode PCA framework. Proc. of the IEEE International Conference on Computer Vision, 2003, 2: 1463-1469
Wang H, Ahuja N. Facial expression decomposition. Proc. of the IEEE International Conference on Computer Vision, 2003, 2: 958-965
Müller K R, Mika S, R?tsch G, Tsuda K, Sch?lkopf B. An introduction to kernel-based learning algorithms. IEEE Transactions on neural networks, 2001, 12(2): 181-202
Vapnik V N. The nature of statistical learning theory. Springer Verlag, New York, 1995
Baudat G, Anouar F. Generalized discriminant analysis using a kernel approach. Neural Computation, 2000, 12(10): 2385-2404
Sch?lkopf B, Smola A J, Müller K R. Nonlinear component analysis as a kernel eigenvalue problem. Neural Computation, 1998, 10: 1299-1319
Mika S, Sch?lkopf B, Smola A J, Müller K R, Scholz M, R?tsch G. Kernel PCA and de-noising in feature spaces. Advances in neural information processing systems 11, 1999: 536-542
Adaptive Simulated Annealing, http://www.ingber.com/ASA-README.html
Pighin F, Szeliski R, Salesin D. Resynthesizing facial animation through 3D model- based tracking. Proc. of the International Conference on Computer Vision, 1999: 143-150
Moubaraki L, Ohya J, Kishino F. Realistic 3D facial animation in virtual space teleconferencing. 4th IEEE International workshop on Robot and Human Communication, 1995: 253-258
Blinn J F. Simulation of wrinkled surfaces. Proc. of the ACM SIGGRAPH Conference on Computer Graphics, 1978: 286-292
Wu Y, Magnenat-Thalmann N, Thalmann D. A plastic-visco-elastic model for wrinkles in facial animation and skin aging. Proc. of Pacific Graphics, 1994: 201-213
Terzopoulos D, Fleisher K. Modeling inelastic deformation: visco-elasticity, plasticity, fracture. Proc. of the ACM SIGGRAPH Conference on Computer Graphics, 1988, 22(4): 269-278
Viad M L, Yahia H. Facial animation with wrinkles. Proc. of the 3rd Eurographics Workshop on Animation and Simulation, 1992.
Moubaraki L, Tanaka H, Kitamura Y, Ohya J, Kishino F. Homotopy-based 3D animation of facial expression. Technical Report, IEICE, IE 94-37, 1994.
Curinga S, Lavageeto F, Vignoli F. Lip movements synthesis using time delay neural networks. Proc. 8th European Signal Process, 1996
Morishima S, Harashima H. A media conversion from speech to facial image for intelligent man-machine interface. IEEE Journal on Selected Areas in Communications, 1991, 9(4): 594-600
Lin I C, Hung C S, Yang T J, Ouhyoung M. A speech driven talking head system based on a single face image. Proc. Seventh Pacific Conference on Computer Graphics and Applications, 1999, 317: 43-49
Ezzat T, Poggio T. Mike talk: a talking facial display based on morphing visemes. Proc. of Computer Animation. IEEE Computer Society, 1998: 96-102
Cosatto E, Potamianos G, Graf H P. Audio-visual unit selection for the synthesis of photo-realistic talking-heads. IEEE International Conference on Multimedia and Expo, 2000, 2: 619-622
Basu S, Oliver N, Pentland A. 3D modeling and tracking of human lip motions. Proc. of the International Conference on Computer Vision, 1998: 337-343
Cohen M, Beskow J, Massaro D W. Recent developments in facial animation: An inside view. Proc. of the International Conference on Auditory-Visual Speech Processing, 1998: 201-206
Yuille A, Hallinan P, Cohen D. Feature extraction from faces using deformable templates. International Journal of Computer Vision 1992, 8(2): 99-111
Kass M, Witkin A, Terzopoulos D. Snakes: active contour models. International Journal of Computer Vision, 1988: 321-331
Hennecke M E, Prasad K V, Stork D G. Using deformable templates to infer visual speech dynamics. IEEE Conference Record of the Asilomar Conference on Signals, Systems & Computers, 1994: 578-582
Shi J, Tomasi C. Good features to track. IEEE Conference on Computer Vision and Pattern Recognition, 1994: 593-600
Prendinger H, Ishizuka M. Simulating affective communication with animated agents. Proc. Eighth IFIP TC.13 Conference on Human-Computer Interaction, 2001, 182-189
Tian Y, Kanade T, Cohn J. Recognizing action units for facial expression analysis IEEE Transactions on Pattern Analysis and Machine Intelligence, 2001, 23(2): 97-115
Zhang Z, Lyons M, Schuster M, Akamatsu S. Comparison between geometry-based and Gabor-wavelets-based facial expression recognition using multi-layer perceptron. Proc. Third IEEE International Conference on Automatic Face and Gesture Recognition, 1998: 454-459
Pantic M, Rothkrantz Leon J M. Automatic analysis of facial expressions: The state of the art. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2000, 22(12): 1424-1445
Williams L. Performance-driven facial animation. Computer Graphics (ACM), 1990, 24(4): 235-242
Litwinowicz P, Williams L. Animating images with drawings. Proc. of the ACM SIGGRAPH Conference on Computer Graphics, 1994: 409-412
Bonamico C, Costa M, Pockaj R, Lavagetto F. Real-time MPEG-4 facial animation with 3D scalable meshes. Signal Processing: Image Communication, 2002, 17(9): 743-757
Lanitis A, Taylor C J, Cootes T F. Toward automatic simulation of aging effects on face images. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2002 24(4): 442-455
Aizawa K, Harashima H, Saito T. Model-based analysis synthesis image coding (MBASIC) system for a person's face. Signal Processing: Image Communication, 1989, 1(2): 139-152
Morishima S. Face analysis and synthesis: For duplication expression and impression. IEEE Signal Processing Magazine, 2001, 18(3): 26-34.
Hong P, Wen Z, Huang T S. Real-time speech-driven face animation with expressions using neural networks. IEEE Transactions on Neural Networks, 2002, 13(4): 916-927
Mukaigawa Y, Nakamura Y, Ohta Y. Face synthesis with arbitrary pose and expression from several images - an integration of image-based and model-based approaches. Proc. of Asian Conference on Computer Vision, 1998: 680-687
Moiza G, Tal A, Shimshoni I, Barnett D, Moses Y. Image-based animation of facial expressions. Visual Computer, 2002, 18(7): 445-465
Picard R W. Affective computing. Media Laboratory, Perceptual Computing TR 321, MIT Media Lab, 1995
Plutchik R. Emotions: a psychoevolutionary synthesis. New York: Harper and Row, 1980
Ortony A, Turner T J. What's basic about basic emotions? Psychological Review, 1990, 97: 315-331
Bradley M. Emotional Memory: A dimensional analysis. Emotions: Essays on emotion theory. Hillsdale, NJ: LEA, 1994
Lang A, Dhillon P, Dong Q. Arousal, emotion, and memory for television messages. Journal of Broadcasting and Electronic Media, 1995, 38: 1-15
Chen H, Xu Y Q, Shum H Y, Zhu S C, Zheng N N. Example-based facial sketch generation with non-parametric sampling. Proc. of the IEEE International Conference on Computer Vision, 2001, 2: 433-438
IBM research, http://www.research.ibm.com/natural/dreamspace/index.html
Microsoft Research, http://www.research.microsoft.com/easyliving/
Romdhani S, Blanz V, Vetter T. Face identification by fitting a 3D morphable model using linear shape and texture error functions. Computer Vision - ECCV 2002, LNCS 2353, 2002: 3-19
Yin L, Basu A. Generating realistic facial expressions with wrinkles for model-based coding. Computer Vision and Image Understanding, 2001, 84(2): 201-240
Abrantes G, Pereira F. MPEG-4 facial animation technology: survey, implementation and results. IEEE Transactions on Circuits and Systems for Video Technology, Special Issue on SNHC, 1999, 9(2) 290-305
MPEG Video, Information technology – Coding of audio-visual objects – Part 2: Visual, Amendment 1: Visual extensions, ISO/IEC JTC 1/SC 29/ WG 11/N3056, January, 2000
Ezzat T, Poggio T. Facial Analysis and synthesis using image-based models. Proc. of the Second International Conference on Automatic Face and Gesture Recognition, 1996: 116-121
Cosatto E, Graf H P. Sample-based synthesis of photo-realistic talking heads. Computer Animation, 1998: 103-110
Bregler C, Covell M, Slaney M. Video rewrite: driving visual speech with audio. Proc. of the ACM SIGGRAPH Conference on Computer Graphics, 1997: 353-360
Parke F I. Computer generated animation of faces. Proc. of the ACM National Conference, 1972: 451-457
Lee Y C, Terzopoulos D, Waters K. Constructing physics-based facial models of individuals. Proc. Graphics Interface, 1993: 1–8
Hong P, Wen Z, Huang T S. iFACE: A 3D synthetic talking face. International Journal of Image and Graphics, 2001, 1(1): 19-26
Blanz V, Vetter T. A morphable model for the synthesis of 3d faces. Proc. of the ACM SIGGRAPH Conference on Computer Graphics, 1999: 187–194
Fujiwara T, Koshimizu H, Fujimura K, Fujita G, Noguchi Y, Ishikawa N. 3D modeling system of human face and full 3D facial caricaturing. Proc. Seventh International Conference on Virtual Systems and Multimedia, 2001: 625–633
Guenter B, Grimm C, Wood D, Malvar D, Pighin F. Making Faces. Proc. of the ACM SIGGRAPH Conference on Computer Graphics, 1998: 55-66
Garcia E, Dugelay J L. Low cost 3D face acquisition and modeling. Proc. International Conference on Information Technology: Coding and Computing, 2001: 657–661
Akimoto T, Suenaga Y, Wallace R S. Automatic creation of 3D facial models. IEEE Computer Graphics and Applications, 1993, 13(3): 16-22
Lee W S, Escher M, Sannier G, Magnenat-Thalmann N. MPEG-4 compatible faces from orthogonal photos. Computer Animation, Conference Proceedings, 1999: 186-194
Goto T, Kshirsagar S, Magnenat-Thalmann N. Automatic face cloning and animation using real-time facial feature tracking and speech acquisition. IEEE Signal Processing Magazine, 2001, 18(3): 17–25
Lee W S, Magnenat-Thalmann N. Fast head modeling for animation. Image and Vision Computing, 2000, 18(4): 355-364
Fua P. Regularized bundle-adjustment to model heads from image sequences without calibration data. International Journal of Computer Vision, 2000, 38(2): 153-171
Shan Y, Liu Z, Zhang Z. Model-based bundle adjustment with application to face modeling. Proc. of International Conference on Computer Vision, 2001, 2: 644-651
Parke F I. A parametric model for human faces. Ph.D. Thesis, University of Utah, Salt Lake City, Utah, UTEC-CSc-75-047, 1974
Ekman P, Friesen W. Manual for the Facial Action Coding System. Consulting Psychologists Press, Palo Alto, 1986
Cohen M, Massara D. Modeling co-articulation in synthetic visual speech. In N. Magnenat Thalmann, D. Thalmann (eds.), Model and Technique in Computer Animation, 1993: 139-156
Haratsch E, Ostermann J. Parameter based animation of arbitrary 3D head models. Proc. of Picture Coding Symposium 1997, VDE Verlag, Berlin, Germany, September 1997: 81-84
Szijártó G, Kiss B, Takács B. Model-based real-time facial animation: design & implementation. In Conf. on Computer Animation & Geometric Modeling, 2002: 30-37
Rydfalk M. CANDIDE, a parameterized face. Report No. LiTH-ISY-I-866, Dept. of Electrical Engineering, Link?ping University, Sweden, 1987
Ahlberg J. CANDIDE-3 -- an updated parameterized face. Report No. LiTH-ISY-R-2326, Dept. of Electrical Engineering, Link?ping University, Sweden, 2001
Platt S, Badler N I. Animating facial expresiion. Computer Graphics, 1981, 15(3): 245-252
Waters K. A muscle model for animating three-dimensional facial expression. Computer Graphics, 1987, 21(4): 17-23
Magnenat-Thalmann N, Primeau E, Thalmann D. Abstract muscle action procedures for human face animation. Visual Computer, 1988, 3(5): 290-297
Terzopoulos D, Waters K. Analysis and synthesis of facial image sequences using physical and anatomical models. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1993, 15(6): 569-579
Lee Y, Terzopoulos D, Waters K. Realistic modeling for facial animation. Proc. of the ACM SIGGRAPH Conference on Computer Graphics, 1995: 55-62
Uz B, Gudukbay U, Ozguc B. Realistic speech animation of synthetic faces. Proc. Computer Animation, 1998: 111~118
Rao R R, Chen T. Audio-to-visual conversion for multimedia communication. IEEE Transactions on Industrial Electronics, 1998, 45(1): 15-22
Perng W L, Wu Y, Ouhyoung M. Image talk: a real time synthetic talking head using one single image with Chinese text-to-speech capability. Sixth Pacific Conference on Computer Graphics and Applications, 1998: 140-148
Arad N, Dyn N, Reisfeld D, Yeshurun Y. Image warping by radial basis functions: application to facial expressions. CVGIP: Graphical Models and Image Processing, 1994, 56(2): 161-172
Zhang Q, Liu Z, Guo B, Shum H. Geometry-driven photorealistic facial expression synthesis. Proc. ACM Symp. on Computer Animation 2003: 16-22
Beier T, Neely S. Feature-based image metamorphosis. Proc. of the ACM SIGGRAPH Conference on Computer Graphics, 1992: 35–42
Benson P J. Morph transformation of the facial image. Image and Vision Computing, 1994, 12(10): 691-696
Pighin F, Hecker J, Lischinski D, Szeliski R, Salesin D H. Synthesizing realistic facial expressions from photographs. Proc. of the ACM SIGGRAPH Conference on Computer Graphics, 1998: 75-84
Seitz S M, Dyer C R. View morphing. Proc. of the ACM SIGGRAPH Conference on Computer Graphics, 1996: 21-42
Shashua A, Riklin-Raviv T. Quotient image: class-based re-rendering and recognition with varying illuminations. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2001, 23(2): 129-139
Stoschek A. Image-based re-rendering of faces for continuous pose and illumination directions. Proc. of the IEEE Conference on Computer Vision and Pattern Recognition, 2000: 582–587
Wen Z, Liu Z, Huang T S. Face relighting with radiance environment maps. Proc. of the IEEE Conference on Computer Vision and Pattern Recognition, 2003: 158-165
Liu Z, Shan Y, Zhang Z. Expressive expression mapping with ratio images. Proc. of the ACM SIGGRAPH Conference on Computer Graphics, 2001: 271-276
Brand M E. Voice Puppetry. Proc. of the ACM SIGGRAPH Conference on Computer Graphics, 1999: 21-28
Yong Noh J, Neumann U. Expression cloning. Proc. of the ACM SIGGRAPH Conference on Computer Graphics, 2001: 277–288
Moghaddam B, Pentland A P. Probabilistic visual learning for object representation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1997, 19(7): 696–710
Turk M, Pentland A. Eigen faces for recognition. Journal of Cognitive Neuroscience, 1991, 3(1): 71~86
Beymer D. Vectorizing face images by interleaving shape and texture computations. M.I.T., A.I. Memo, No. 1537, 1995
Troje N F, Vetter T. Representations of human faces. Max-Planck-Institut fur biologische Kybernetik, Tubingen. MPI-memo No. 41, 1996
Vetter T, Poggio T. Linear object classes and image synthesis from a single example image. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1997, 19(7): 733-742
Vetter T. Synthesis of novel views from a single face image. International Journal of Computer Vision, 1998, 28(2): 103-116
Lanitis A, Taylor C J, Cootes T F. Automatic interpretation and coding of face images using flexible models. IEEE Transactions on Pattern Analysis and Machine Intelligence 1997, 19(7): 743-756
Romdhani S, Vetter T. Efficient, Robust and Accurate Fitting of a 3D Morphable Model. Proc. International Conference on Computer Vision, 2003, 2: 59-66
Cooper D H, Cootes T F, Taylor C J, Graham J. Active shape models - their training and application. Computer Vision and Image Understanding, 1995, 61(1): 38-59
Cootes T F, Taylor C J, Lanitis A. Multi-resolution search using active shape models. Proc. 12th International Conference on Pattern Recognition, 1994, 1: 610-612
Sim T, Baker S, Bsat M. The CMU pose, illumination, expression database. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2003, 25(12): 1615-1618.
Kanade T, Cohn J F, Tian Y. Comprehensive database for facial expression analysis. Proc. of the Fourth IEEE International Conference on Automatic Face and Gesture Recognition, 2000: 46-53
Lyons M J, Budynek J, Akamatsu S. Automatic classification of single facial images. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1999, 21(12): 1357-1362
Georghiades A S, Belhumeur P N, Kriegman D J. From few to many: illumination cone models for face recognition under variable lighting and pose. IEEE Transactions Pattern Analysis and Machine Intelligence, 2001, 23(6): 643-660
Phillips P J, Moon H, Rauss P J, Rizvi S. The FERET evaluation methodology for face recognition algorithms. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2000, 22(10): 1090-1104
Loui A C, Judice C N, Liu S. An image database for benchmarking of automatic face detection and recognition algorithms. Proc. of International Conference Image Processing, 1998: 146~150
Graham D B, Allinson N M. Characterizing virtual eigensignatures for general purpose face recognition. Face Recognition: From Theory to Applications, Sprigerverlag, 1998, 163: 446~456
The AR Face Database, http://rvl1.ecn.purdue.edu/~aleix/aleix_face_DB.html
CVL Face Database, http://www.lrv.fri.uni-lj.si/facedb.html
XM2VTSDB, http://xm2vtsdb.ee.surrey.ac.uk/home.html
Mukaigawa Y, Nakamura Y, Ohta Y. Synthesis of arbitrarily oriented face views from two images. Proc. of Asian Conference on Computer Vision, 1995, 3: 718-722
Ullman S, Basri R. Recognition by linear combinations of models. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1991, 13(10): 992-1006
Mukaigawa Y, Nakamura Y, Ohta Y. Facial animation from several images. Proc. of International Symposium on Real-Time Imaging and Dynamic Analysis, 1998, II(5), 906-911
Cootes T F, Wheeler G V, Walker K N, Taylor C J. View-based active appearance models. Image and Vision Computing, 2002, 20(9-10): 657-664
Okada K, Akamatsu S, Von der Malsburg C. Analysis and synthesis of pose variations of human faces by a linear PCMAP Model and its application for pose-invariant face recognition system. Proc. of Fourth International Conference on Automatic Face and Gesture Recognition, 2000: 142-149
Okada K, Von der Malsburg C. Analysis and synthesis of human faces with pose variations by a parametric piecewise linear subspace method. Proc. of the IEEE Conference on Computer Vision and Pattern Recognition, 2001, 1: I761-I768
Romdhani S, Gong S, Psarrou A. A multi-view non-linear active shape model using kernel PCA. Proc. 10-th British Machine Vision Conference, 1999, 2: 483-492
Edelman S, Weinshall D, Yeshurun Y. Learning to recognize faces from examples. Proc. of the 2nd European Conference on Computer Vision, 1992: 787–791
Lando M, Edelman S. Generalization from a single view in face recognition. Proc. of the International Workshop on Automatic Face and Gesture Recognition, 1995: 80-85
D'Zmura M, Iverson G. Color constancy: I. Basic theory of two-stage linear recovery of spectral descriptions for lights and surfaces. Journal of the Optical Society of America A: Optics and Image Science, 1993, 10(10): 2148-2165
Tomasi C, Kanade T. Shape and motion from image streams under orthography: a factorization method. International Journal of Computer Vision, 1992, 9(2): 137-154
Tenenbaum J B, Freeman W T. Separating style and content with bilinear models. Neural Computation, 2000, 12: 1247-1283
Chuang E, Deshpande H, Bregler C. Facial expression space learning. Proc. of Pacific Graphics, 2002: 68-76
Grimes D B, Shon A P, Rao R P N. Probabilistic bilinear models for appearance- based vision. Proc. of the IEEE International Conference on Computer Vision, 2003, 2: 1478-1485
Du Y, Lin X. Nonlinear factorization models using kernel approaches. Proc. Asian Conference on Computer Vision, 2004, 1: 426-431
Davis J W, Gao H. Recognizing human action efforts: An adaptive three-mode PCA framework. Proc. of the IEEE International Conference on Computer Vision, 2003, 2: 1463-1469
Wang H, Ahuja N. Facial expression decomposition. Proc. of the IEEE International Conference on Computer Vision, 2003, 2: 958-965
Müller K R, Mika S, R?tsch G, Tsuda K, Sch?lkopf B. An introduction to kernel-based learning algorithms. IEEE Transactions on neural networks, 2001, 12(2): 181-202
Vapnik V N. The nature of statistical learning theory. Springer Verlag, New York, 1995
Baudat G, Anouar F. Generalized discriminant analysis using a kernel approach. Neural Computation, 2000, 12(10): 2385-2404
Sch?lkopf B, Smola A J, Müller K R. Nonlinear component analysis as a kernel eigenvalue problem. Neural Computation, 1998, 10: 1299-1319
Mika S, Sch?lkopf B, Smola A J, Müller K R, Scholz M, R?tsch G. Kernel PCA and de-noising in feature spaces. Advances in neural information processing systems 11, 1999: 536-542
Adaptive Simulated Annealing, http://www.ingber.com/ASA-README.html
Pighin F, Szeliski R, Salesin D. Resynthesizing facial animation through 3D model- based tracking. Proc. of the International Conference on Computer Vision, 1999: 143-150
Moubaraki L, Ohya J, Kishino F. Realistic 3D facial animation in virtual space teleconferencing. 4th IEEE International workshop on Robot and Human Communication, 1995: 253-258
Blinn J F. Simulation of wrinkled surfaces. Proc. of the ACM SIGGRAPH Conference on Computer Graphics, 1978: 286-292
Wu Y, Magnenat-Thalmann N, Thalmann D. A plastic-visco-elastic model for wrinkles in facial animation and skin aging. Proc. of Pacific Graphics, 1994: 201-213
Terzopoulos D, Fleisher K. Modeling inelastic deformation: visco-elasticity, plasticity, fracture. Proc. of the ACM SIGGRAPH Conference on Computer Graphics, 1988, 22(4): 269-278
Viad M L, Yahia H. Facial animation with wrinkles. Proc. of the 3rd Eurographics Workshop on Animation and Simulation, 1992.
Moubaraki L, Tanaka H, Kitamura Y, Ohya J, Kishino F. Homotopy-based 3D animation of facial expression. Technical Report, IEICE, IE 94-37, 1994.
Curinga S, Lavageeto F, Vignoli F. Lip movements synthesis using time delay neural networks. Proc. 8th European Signal Process, 1996
Morishima S, Harashima H. A media conversion from speech to facial image for intelligent man-machine interface. IEEE Journal on Selected Areas in Communications, 1991, 9(4): 594-600
Lin I C, Hung C S, Yang T J, Ouhyoung M. A speech driven talking head system based on a single face image. Proc. Seventh Pacific Conference on Computer Graphics and Applications, 1999, 317: 43-49
Ezzat T, Poggio T. Mike talk: a talking facial display based on morphing visemes. Proc. of Computer Animation. IEEE Computer Society, 1998: 96-102
Cosatto E, Potamianos G, Graf H P. Audio-visual unit selection for the synthesis of photo-realistic talking-heads. IEEE International Conference on Multimedia and Expo, 2000, 2: 619-622
Basu S, Oliver N, Pentland A. 3D modeling and tracking of human lip motions. Proc. of the International Conference on Computer Vision, 1998: 337-343
Cohen M, Beskow J, Massaro D W. Recent developments in facial animation: An inside view. Proc. of the International Conference on Auditory-Visual Speech Processing, 1998: 201-206
Yuille A, Hallinan P, Cohen D. Feature extraction from faces using deformable templates. International Journal of Computer Vision 1992, 8(2): 99-111
Kass M, Witkin A, Terzopoulos D. Snakes: active contour models. International Journal of Computer Vision, 1988: 321-331
Hennecke M E, Prasad K V, Stork D G. Using deformable templates to infer visual speech dynamics. IEEE Conference Record of the Asilomar Conference on Signals, Systems & Computers, 1994: 578-582
Shi J, Tomasi C. Good features to track. IEEE Conference on Computer Vision and Pattern Recognition, 1994: 593-600
Prendinger H, Ishizuka M. Simulating affective communication with animated agents. Proc. Eighth IFIP TC.13 Conference on Human-Computer Interaction, 2001, 182-189
Tian Y, Kanade T, Cohn J. Recognizing action units for facial expression analysis IEEE Transactions on Pattern Analysis and Machine Intelligence, 2001, 23(2): 97-115
Zhang Z, Lyons M, Schuster M, Akamatsu S. Comparison between geometry-based and Gabor-wavelets-based facial expression recognition using multi-layer perceptron. Proc. Third IEEE International Conference on Automatic Face and Gesture Recognition, 1998: 454-459
Pantic M, Rothkrantz Leon J M. Automatic analysis of facial expressions: The state of the art. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2000, 22(12): 1424-1445
Williams L. Performance-driven facial animation. Computer Graphics (ACM), 1990, 24(4): 235-242
Litwinowicz P, Williams L. Animating images with drawings. Proc. of the ACM SIGGRAPH Conference on Computer Graphics, 1994: 409-412
Bonamico C, Costa M, Pockaj R, Lavagetto F. Real-time MPEG-4 facial animation with 3D scalable meshes. Signal Processing: Image Communication, 2002, 17(9): 743-757
Lanitis A, Taylor C J, Cootes T F. Toward automatic simulation of aging effects on face images. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2002 24(4): 442-455
Aizawa K, Harashima H, Saito T. Model-based analysis synthesis image coding (MBASIC) system for a person's face. Signal Processing: Image Communication, 1989, 1(2): 139-152
Morishima S. Face analysis and synthesis: For duplication expression and impression. IEEE Signal Processing Magazine, 2001, 18(3): 26-34.
Hong P, Wen Z, Huang T S. Real-time speech-driven face animation with expressions using neural networks. IEEE Transactions on Neural Networks, 2002, 13(4): 916-927
Mukaigawa Y, Nakamura Y, Ohta Y. Face synthesis with arbitrary pose and expression from several images - an integration of image-based and model-based approaches. Proc. of Asian Conference on Computer Vision, 1998: 680-687
Moiza G, Tal A, Shimshoni I, Barnett D, Moses Y. Image-based animation of facial expressions. Visual Computer, 2002, 18(7): 445-465
Picard R W. Affective computing. Media Laboratory, Perceptual Computing TR 321, MIT Media Lab, 1995
Plutchik R. Emotions: a psychoevolutionary synthesis. New York: Harper and Row, 1980
Ortony A, Turner T J. What's basic about basic emotions? Psychological Review, 1990, 97: 315-331
Bradley M. Emotional Memory: A dimensional analysis. Emotions: Essays on emotion theory. Hillsdale, NJ: LEA, 1994
Lang A, Dhillon P, Dong Q. Arousal, emotion, and memory for television messages. Journal of Broadcasting and Electronic Media, 1995, 38: 1-15
Chen H, Xu Y Q, Shum H Y, Zhu S C, Zheng N N. Example-based facial sketch generation with non-parametric sampling. Proc. of the IEEE International Conference on Computer Vision, 2001, 2: 433-438
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