Analysis, synthesis and recognition of human faces with pose variations

Face recognition is one of the most interesting and challenging problems in computer vision. A great difficulty in face recognition is the separation of intrinsic facial characteristics from extrinsic image variations. Among the latter in 2D images are pose, illumination, and expression. Unfortunately, most past studies have provided variation-specific solutions that are not applicable to other types of variation. Performance has remained inferior to human ability and sub-optimal for practical use. Our investigation focuses on one of these problems, which is processing head pose information in 2D images: analyzing, synthesizing, and identifying facial images with arbitrary pose. Our goal is twofold. One is to provide a simple and general framework which may be useful beyond the specific problem of head pose. The other is to improve the pose processing accuracy of previous studies by using this framework. We propose a localized two-stage linear system which is learned strictly from sample statistics and models shape and texture information separately. Instead of using domain-specific analytical knowledge of 3D rotation in Euclidean space, our solution utilizes a simple statistical learning framework whose applicability is not limited to the problem at hand. A wider range of head poses is covered by a number of local linear models distributed over various poses, each of which realizes a continuous mapping function which directly associates a face's representation with the corresponding 3D head angle. Our experiments prove the system to be very accurate in terms of pose estimation and image reconstruction as a function of pose while covering a greater pose range and a higher number of rotation dimensions than previous systems. The systems generalization capability over unknown poses and persons is also shown. This facilitates continuous and smooth coverage of pose variation and learning from few samples. The main contribution of this study is a solution that is simultaneously simple, general and accurate. Its data-driven nature simplifies an otherwise labor-intensive data collection procedure. Possible applications include on-line sequential learning systems and man-machine communication.

[1]  J. Baumert,et al.  Longitudinal and multi-group modeling with missing data , 2022 .

[2]  Christopher G. Atkeson,et al.  Constructive Incremental Learning from Only Local Information , 1998, Neural Computation.

[3]  Nicholas Costen,et al.  Automatic Face Recognition: What Representation? , 1996, ECCV.

[4]  Laurenz Wiskott,et al.  Phantom faces for face analysis , 1997, Proceedings of International Conference on Image Processing.

[5]  Martha J. Farah.  [Visual agnosia]. , 1971, Shinkei kenkyu no shimpo. Advances in neurological sciences.

[6]  Shaogang Gong,et al.  Learning to Associate Faces across Views in Vector Space of Similarities to Prototypes , 1998, BMVC.

[7]  Rama Chellappa,et al.  Human and machine recognition of faces: a survey , 1995, Proc. IEEE.

[8]  C. Gross,et al.  Representation of visual stimuli in inferior temporal cortex. , 1992, Philosophical transactions of the Royal Society of London. Series B, Biological sciences.

[9]  Shaogang Gong,et al.  Multi-view face detection and pose estimation using a composite support vector machine across the view sphere , 1999, Proceedings International Workshop on Recognition, Analysis, and Tracking of Faces and Gestures in Real-Time Systems. In Conjunction with ICCV'99 (Cat. No.PR00378).

[10]  Irving Biederman,et al.  Visual object recognition , 1993 .

[11]  R. M. Siegel,et al.  Modulation of responses to optic flow in area 7a by retinotopic and oculomotor cues in monkey. , 1997, Cerebral cortex.

[12]  Timothy F. Cootes,et al.  Automatic interpretation of human faces and hand gestures using flexible models. , 1995 .

[13]  D. B. Graham,et al.  Face recognition from unfamiliar views: subspace methods and pose dependency , 1998, Proceedings Third IEEE International Conference on Automatic Face and Gesture Recognition.

[14]  Shaogang Gong,et al.  Fusion of 2D face alignment and 3D head pose estimation for robust and real-time performance , 1999, Proceedings International Workshop on Recognition, Analysis, and Tracking of Faces and Gestures in Real-Time Systems. In Conjunction with ICCV'99 (Cat. No.PR00378).

[15]  Saburo Tsuji,et al.  Recognizing human facial expressions in a potential field , 1994, Proceedings of the 12th IAPR International Conference on Pattern Recognition, Vol. 3 - Conference C: Signal Processing (Cat. No.94CH3440-5).

[16]  David Beymer,et al.  Face recognition from one example view , 1995, Proceedings of IEEE International Conference on Computer Vision.

[17]  V. Bruce,et al.  A comparison of two computer-based face identification systems with human perceptions of faces , 1998, Vision Research.

[18]  Alex Pentland,et al.  An automatic system for model-based coding of faces , 1995, Proceedings DCC '95 Data Compression Conference.

[19]  A. Cowey,et al.  The role of the 'face-cell' area in the discrimination and recognition of faces by monkeys. , 1992, Philosophical transactions of the Royal Society of London. Series B, Biological sciences.

[20]  Timothy F. Cootes,et al.  A unified approach to coding and interpreting face images , 1995, Proceedings of IEEE International Conference on Computer Vision.

[21]  Ronen Basri,et al.  Recognition by Linear Combinations of Models , 1991, IEEE Trans. Pattern Anal. Mach. Intell..

[22]  Kiyoharu Aizawa,et al.  Model-based analysis synthesis image coding (MBASIC) system for a person's face , 1989, Signal Process. Image Commun..

[23]  Hsueh-Ming Hang,et al.  Adaptive Piecewise Linear Bits Estimation Model for MPEG Based Video Coding , 1997, J. Vis. Commun. Image Represent..

[24]  Erkki Oja,et al.  Principal components, minor components, and linear neural networks , 1992, Neural Networks.

[25]  Martin Lades,et al.  Invariant object recognition with dynamical links, robust to variations in illumination , 1994 .

[26]  L Sirovich,et al.  Low-dimensional procedure for the characterization of human faces. , 1987, Journal of the Optical Society of America. A, Optics and image science.

[27]  Y. J. Tejwani,et al.  Robot vision , 1989, IEEE International Symposium on Circuits and Systems,.

[28]  Richard A. Andersen,et al.  Separate body- and world-referenced representations of visual space in parietal cortex , 1998, Nature.

[29]  Michael J. Black,et al.  Recognizing facial expressions under rigid and non-rigid facial motions , 1995 .

[30]  Xinhua Zhuang,et al.  Piecewise linear classifiers using binary tree structure and genetic algorithm , 1996, Pattern Recognit..

[31]  Thomas Vetter,et al.  A morphable model for the synthesis of 3D faces , 1999, SIGGRAPH.

[32]  Hyeonjoon Moon,et al.  The FERET evaluation methodology for face-recognition algorithms , 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[33]  Gaile G. Gordon,et al.  Face recognition based on depth and curvature features , 1992, Proceedings 1992 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[34]  Terrence J. Sejnowski,et al.  The “independent components” of natural scenes are edge filters , 1997, Vision Research.

[35]  Xiaobo Li,et al.  An improved piecewise approximation algorithm for image compression , 1998, Pattern Recognit..

[36]  D. B. Gerham.  Characterizing virtual eigensignatures for general purpose face recognition , 1998 .

[37]  Tomaso Poggio,et al.  Example Based Image Analysis and Synthesis , 1993 .

[38]  James L. Arbuckle,et al.  Full Information Estimation in the Presence of Incomplete Data , 1996 .

[39]  Roberto Brunelli,et al.  Face Recognition through Geometrical Features , 1992, ECCV.

[40]  Ah Chung Tsoi,et al.  Face Recognition: A Hybrid Neural Network Approach , 1998 .

[41]  Christoph von der Malsburg,et al.  Learning Feature Transformations to Recognize Faces Rotated in Depth , 1995 .

[42]  Marian Stewart Bartlett,et al.  Independent component representations for face recognition , 1998, Electronic Imaging.

[43]  John G. Daugman,et al.  Complete discrete 2-D Gabor transforms by neural networks for image analysis and compression , 1988, IEEE Trans. Acoust. Speech Signal Process..

[44]  Pascal Fua,et al.  Using differential constraints to generate a 3D face model from stereo , 1998, Proceedings. Fourteenth International Conference on Pattern Recognition (Cat. No.98EX170).

[45]  Christoph von der Malsburg,et al.  A Pose-Invariant Face Recognition System using Linear PCMAP Model (特集テーマ・顔・表情・ジェスチャの認識・合成) -- (顔の認識・生成) , 1999 .

[46]  Yulia Kempner,et al.  Application of piece-wise regression to detecting internal structure of signal , 1992, Pattern Recognit..

[47]  EdelmanFlorin CutzuSharon Duvdevani-BarDept.  Similarity to reference shapes as a basis for shaperepresentationShimon , 1996 .

[48]  R. Andersen,et al.  Multimodal representation of space in the posterior parietal cortex and its use in planning movements. , 1997, Annual review of neuroscience.

[49]  G. G. Stokes.  "J." , 1890, The New Yale Book of Quotations.

[50]  T. Sakai,et al.  Computer analysis and classification of photographs of human faces , 1973 .

[51]  Syed A. Rizvi,et al.  The FERET Evaluation , 1998 .

[52]  M. Turk,et al.  Eigenfaces for Recognition , 1991, Journal of Cognitive Neuroscience.

[53]  Christopher M. Bishop,et al.  Mixtures of Probabilistic Principal Component Analyzers , 1999, Neural Computation.

[54]  C. Malsburg,et al.  Determination of Face Position and Pose with a Learned Representation Based on Labeled Graphs Determination of Face Position and Pose with a Learned Representation Based on Labeled Graphs , 1996 .

[55]  Paul W. Munro,et al.  Principal Components Analysis Of Images Via Back Propagation , 1988, Other Conferences.

[56]  Alex Pentland,et al.  Beyond eigenfaces: probabilistic matching for face recognition , 1998, Proceedings Third IEEE International Conference on Automatic Face and Gesture Recognition.

[57]  Koichiro Deguchi,et al.  Head pose determination from one image using a generic model , 1998, Proceedings Third IEEE International Conference on Automatic Face and Gesture Recognition.

[58]  Mark D. Plumbley.  A Network Which Performs Orthonormalized Principal Subspace Extraction , 1994 .

[59]  Ming Xu,et al.  Detecting head pose from stereo image sequence for active face recognition , 1998, Proceedings Third IEEE International Conference on Automatic Face and Gesture Recognition.

[60]  Tomaso A. Poggio,et al.  Linear Object Classes and Image Synthesis From a Single Example Image , 1997, IEEE Trans. Pattern Anal. Mach. Intell..

[61]  Rolf P. Würtz,et al.  Multilayer dynamic link networks for establishing image point correspondences and visual object recognition , 1995 .

[62]  Ronen Basri,et al.  Recognition by prototypes , 1993, Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.

[63]  Christoph von der Malsburg,et al.  Tracking and learning graphs and pose on image sequences of faces , 1996, Proceedings of the Second International Conference on Automatic Face and Gesture Recognition.

[64]  Timothy F. Cootes,et al.  Active Shape Models-Their Training and Application , 1995, Comput. Vis. Image Underst..

[65]  Jeanny Hérault,et al.  Curvilinear component analysis: a self-organizing neural network for nonlinear mapping of data sets , 1997, IEEE Trans. Neural Networks.

[66]  A. Young,et al.  Face recognition impairments. , 1992, Philosophical transactions of the Royal Society of London. Series B, Biological sciences.

[67]  Mark D. Plumbley.  A Hebbian/anti-Hebbian network which optimizes information capacity by orthonormalizing the principal subspace , 1993 .

[68]  Christoph von der Malsburg,et al.  Automatic video indexing with incremental gallery creation: integration of recognition and knowledge acquisition , 1999, 1999 Third International Conference on Knowledge-Based Intelligent Information Engineering Systems. Proceedings (Cat. No.99TH8410).

[69]  Luc Van Gool,et al.  Getting facial features and gestures in 3D , 1998 .

[70]  E. Oja,et al.  On stochastic approximation of the eigenvectors and eigenvalues of the expectation of a random matrix , 1985 .

[71]  Hyeonjoon Moon,et al.  The FERET September 1996 Database and Evaluation Procedure , 1997, AVBPA.

[72]  Federico Girosi,et al.  Training support vector machines: an application to face detection , 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[73]  Laurenz Wiskott,et al.  Labeled graphs and dynamic link matching for face recognition and scene analysis , 1995 .

[74]  Andrzej Cichocki,et al.  A New Learning Algorithm for Blind Signal Separation , 1995, NIPS.

[75]  D. B. Bender,et al.  Visual properties of neurons in inferotemporal cortex of the Macaque. , 1972, Journal of neurophysiology.

[76]  Shimon Edelman,et al.  Bringing the Grandmother back into the Picture: A Memory-Based View of Object Recognition , 1990, Int. J. Pattern Recognit. Artif. Intell..

[77]  F. A. Seiler,et al.  Numerical Recipes in C: The Art of Scientific Computing , 1989 .

[78]  F. Girosi,et al.  Networks for approximation and learning , 1990, Proc. IEEE.

[79]  Gérard G. Medioni,et al.  Segmented shape descriptions from 3-view stereo , 1995, Proceedings of IEEE International Conference on Computer Vision.

[80]  Alice J. O'Toole,et al.  CATEGORIZATION AND IDENTIFICATION OF HUMAN FACE IMAGES BY NEURAL NETWORKS: A REVIEW OF THE LINEAR AUTOASSOCIATIVE AND PRINCIPAL COMPONENT APPROACHES , 1994 .

[81]  S Edelman,et al.  A model of visual recognition and categorization. , 1997, Philosophical transactions of the Royal Society of London. Series B, Biological sciences.

[82]  JEAN-MARC FELLOUS,et al.  PII: S0042-6989(97)00010-2 , 2003 .

[83]  Bernhard Schölkopf,et al.  Nonlinear Component Analysis as a Kernel Eigenvalue Problem , 1998, Neural Computation.

[84]  Scott T. Acton,et al.  Piecewise and local image models for regularized image restoration using cross-validation , 1999, IEEE Trans. Image Process..

[85]  Thomas Vetter,et al.  Generalization to Novel Views from a Single Face Image , 1998 .

[86]  Michael I. Jordan,et al.  Learning from Incomplete Data , 1994 .

[87]  V. Bruce,et al.  Recognizing objects and faces , 1994 .

[88]  Ian Craw,et al.  Face Recognition by Computer , 1992, BMVC.

[89]  Robert M. Haralick,et al.  2D-3D pose estimation , 1988, [1988 Proceedings] 9th International Conference on Pattern Recognition.

[90]  R A Johnston,et al.  Understanding face recognition with an interactive activation model. , 1990, British journal of psychology.

[91]  Aapo Hyvärinen,et al.  Independent Component Analysis: A Tutorial , 1999 .

[92]  Gabor T. Herman,et al.  On Piecewise-Linear Classification , 1992, IEEE Trans. Pattern Anal. Mach. Intell..

[93]  Roberto Brunelli,et al.  Estimation of pose and illuminant direction for face processing , 1994, Image Vis. Comput..

[94]  Hilary Buxton,et al.  A similarity-based method for the generalization of face recognition over pose and expression , 1998, Proceedings Third IEEE International Conference on Automatic Face and Gesture Recognition.

[95]  Jean-Christophe Terrillon,et al.  Skin-Color Based Segmentation and Invariant Detection of Human Faces in still Scene Images by Use of Support Vector Machines (特集テーマ・顔・表情・ジェスチャの認識・合成) -- (顔の認識・生成) , 1999 .

[96]  Qian Chen,et al.  3D head pose estimation without feature tracking , 1998, Proceedings Third IEEE International Conference on Automatic Face and Gesture Recognition.

[97]  Yukio Sato,et al.  Piecewise linear approximation of plane curves by perimeter optimization , 1992, Pattern Recognit..

[98]  Y. Sato,et al.  Three-dimensional shape reconstruction by active rangefinder , 1993, Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.

[99]  F. I. Parke June,et al.  Computer Generated Animation of Faces , 1972 .

[100]  Bernd Fritzke.  Incremental Learning of Local Linear Mappings , 1995 .

[101]  R. Weale.  Vision. A Computational Investigation Into the Human Representation and Processing of Visual Information. David Marr , 1983 .

[102]  Jan Wieghardt,et al.  Pose-Independent Object Representation by 2-D Views , 2000, Biologically Motivated Computer Vision.

[103]  Terrence J. Sejnowski,et al.  An Information-Maximization Approach to Blind Separation and Blind Deconvolution , 1995, Neural Computation.

[104]  Lei Xu,et al.  Least mean square error reconstruction principle for self-organizing neural-nets , 1993, Neural Networks.

[105]  Roberto Brunelli,et al.  Face Recognition: Features Versus Templates , 1993, IEEE Trans. Pattern Anal. Mach. Intell..

[106]  Shaogang Gong,et al.  Support vector regression and classification based multi-view face detection and recognition , 2000, Proceedings Fourth IEEE International Conference on Automatic Face and Gesture Recognition (Cat. No. PR00580).

[107]  Rama Chellappa,et al.  Discriminant analysis of principal components for face recognition , 1998, Proceedings Third IEEE International Conference on Automatic Face and Gesture Recognition.

[108]  Christoph von der Malsburg,et al.  Single-View Based Recognition of Faces Rotated in Depth , 1995 .

[109]  Heekuck Oh,et al.  Neural Networks for Pattern Recognition , 1993, Adv. Comput..

[110]  Alexander Zelinsky,et al.  3-D facial pose and gaze point estimation using a robust real-time tracking paradigm , 1998, Proceedings Third IEEE International Conference on Automatic Face and Gesture Recognition.

[111]  David J. Kriegman,et al.  From few to many: generative models for recognition under variable pose and illumination , 2000, Proceedings Fourth IEEE International Conference on Automatic Face and Gesture Recognition (Cat. No. PR00580).

[112]  Penio S. Penev,et al.  Local feature analysis: A general statistical theory for object representation , 1996 .

[113]  Timothy F. Cootes,et al.  Automatic Interpretation and Coding of Face Images Using Flexible Models , 1997, IEEE Trans. Pattern Anal. Mach. Intell..

[114]  D I Perrett,et al.  Organization and functions of cells responsive to faces in the temporal cortex. , 1992, Philosophical transactions of the Royal Society of London. Series B, Biological sciences.

[115]  Elie Bienenstock,et al.  Neural Networks and the Bias/Variance Dilemma , 1992, Neural Computation.

[116]  Joshua B. Tenenbaum,et al.  Mapping a Manifold of Perceptual Observations , 1997, NIPS.

[117]  Kurt Hornik,et al.  Learning in linear neural networks: a survey , 1995, IEEE Trans. Neural Networks.

[118]  I Biederman,et al.  Neurocomputational bases of object and face recognition. , 1997, Philosophical transactions of the Royal Society of London. Series B, Biological sciences.

[119]  Timothy F. Cootes,et al.  Interpreting face images using active appearance models , 1998, Proceedings Third IEEE International Conference on Automatic Face and Gesture Recognition.

[120]  Chil-Woo Lee,et al.  Detection and pose estimation of human face with synthesized image models , 1994, Proceedings of 12th International Conference on Pattern Recognition.

[121]  D. Rubin,et al.  Statistical Analysis with Missing Data , 1988 .

[122]  Jörg A. Walter.  Rapid learning in robotics , 1996 .

[123]  Tsuhan Chen,et al.  Pose invariant face recognition , 2000, Proceedings Fourth IEEE International Conference on Automatic Face and Gesture Recognition (Cat. No. PR00580).

[124]  Hartmut Neven,et al.  PersonSpotter-fast and robust system for human detection, tracking and recognition , 1998, Proceedings Third IEEE International Conference on Automatic Face and Gesture Recognition.

[125]  Christoph von der Malsburg,et al.  Reconstruction from Graphs Labeled with Responses of Gabor Filters , 1996, ICANN.

[126]  Mineichi Kudo,et al.  Piecewise linear classifiers with an appropriate number of hyperplanes , 1998, Pattern Recognit..

[127]  Takeo Kanade,et al.  Human Face Detection in Visual Scenes , 1995, NIPS.

[128]  Ashok Samal,et al.  Automatic recognition and analysis of human faces and facial expressions: a survey , 1992, Pattern Recognit..

[129]  Vladimir N. Vapnik,et al.  The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.

[130]  R. Desimone.  Face-Selective Cells in the Temporal Cortex of Monkeys , 1991, Journal of Cognitive Neuroscience.

[131]  Edwin R. Hancock,et al.  Estimating 3D Facial Pose using the EM Algorithm , 1998 .

[132]  Jack Sklansky,et al.  Neural modeling of piecewise linear classifiers , 1996, Proceedings of 13th International Conference on Pattern Recognition.

[133]  Marian Stewart Bartlett,et al.  Viewpoint Invariant Face Recognition using Independent Component Analysis and Attractor Networks , 1996, NIPS.

[134]  Shimon Edelman,et al.  Receptive field spaces and class-based generalization from a single view in face recognition , 1995 .

[135]  Y. Chauvin,et al.  Principal component analysis by gradient descent on a constrained linear Hebbian cell , 1989, International 1989 Joint Conference on Neural Networks.

[136]  Peter J. B. Hancock,et al.  Comparisons between human and computer recognition of faces , 1998, Proceedings Third IEEE International Conference on Automatic Face and Gesture Recognition.

[137]  Alex Pentland,et al.  View-based and modular eigenspaces for face recognition , 1994, 1994 Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.

[138]  Timothy F. Cootes,et al.  The Use of Active Shape Models for Locating Structures in Medical Images , 1993, IPMI.

[139]  T. Vetter,et al.  A separated linear shape and texture space for modeling two-dimensional images of human faces , 1995 .

[140]  S. Akamatsu,et al.  An ICA based representation of facial images and its application for face recognition systems , 2022 .

[141]  P. Foldiak,et al.  Adaptive network for optimal linear feature extraction , 1989, International 1989 Joint Conference on Neural Networks.

[142]  Seiichi Uchida,et al.  Piecewise linear two-dimensional warping , 2000, Proceedings 15th International Conference on Pattern Recognition. ICPR-2000.

[143]  Terrence J. Sejnowski,et al.  Nonlinear Time-Series Prediction with Missing and Noisy Data , 2001 .

[144]  Roberto Brunelli,et al.  MASSACHUSETTS INSTITUTE OF TECHNOLOGY ARTIFICIAL INTELLIGENCE LABORATORY and CENTER FOR BIOLOGICAL AND COMPUTATIONAL LEARNING DEPARTMENT OF BRAIN AND COGNITIVE SCIENCES , 2001 .

[145]  V Bruce,et al.  Modelling face recognition. , 1992, Philosophical transactions of the Royal Society of London. Series B, Biological sciences.

[146]  Niels da Vitoria Lobo,et al.  Face detection using templates , 1994, Proceedings of 12th International Conference on Pattern Recognition.

[147]  A. Young,et al.  Understanding face recognition. , 1986, British journal of psychology.

[148]  Tomaso A. Poggio,et al.  A bootstrapping algorithm for learning linear models of object classes , 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[149]  Sandor Z. Der,et al.  FERET (Face Recognition Technology) Recognition Algorithm Development and Test Results. , 1996 .

[150]  David,et al.  Pose Discriminiation and Eye Detection Using Support Vector Machines (SVM) , 1998 .

[151]  G. Gordon.  3D Pose Estimation of the Face from Video , 1998 .

[152]  Alice J. O'Toole,et al.  Connectionist models of face processing: A survey , 1994, Pattern Recognit..

[153]  Rama Chellappa,et al.  SFS based view synthesis for robust face recognition , 2000, Proceedings Fourth IEEE International Conference on Automatic Face and Gesture Recognition (Cat. No. PR00580).

[154]  I. Biederman,et al.  Neural and psychophysical analysis of object and face recognition , 1998 .

[155]  Teuvo Kohonen,et al.  Associative memory. A system-theoretical approach , 1977 .

[156]  W. J. Welsh,et al.  Classification of facial features for recognition , 1991, Proceedings. 1991 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[157]  Kartik Venkataraman,et al.  Piece-wise linear morphing and rendering with 3D textures , 1997, Comput. Networks ISDN Syst..

[158]  Marian Stewart Bartlett,et al.  Independent components of face images : A representation for face recognition , 1997 .

[159]  Marian Stewart Bartlett,et al.  Classifying Facial Actions , 1999, IEEE Trans. Pattern Anal. Mach. Intell..

[160]  Shaogang Gong,et al.  Real-time face pose estimation , 1998, Real Time Imaging.

[161]  Peter E. Hart,et al.  Pattern classification and scene analysis , 1974, A Wiley-Interscience publication.

[162]  Gaile G. Gordon,et al.  Face recognition from frontal and profile views , 1995 .

[163]  E T Rolls,et al.  Neurophysiological mechanisms underlying face processing within and beyond the temporal cortical visual areas. , 1992, Philosophical transactions of the Royal Society of London. Series B, Biological sciences.

[164]  Timothy F. Cootes,et al.  Learning to identify and track faces in image sequences , 1998, Proceedings Third IEEE International Conference on Automatic Face and Gesture Recognition.

[165]  Wenyi Zhao,et al.  Face similarity space as perceived by humans and artificial systems , 1998, Proceedings Third IEEE International Conference on Automatic Face and Gesture Recognition.

[166]  L. B. Muñoz.  Heterogeneous neural networks: theory and applications , 2000 .

[167]  A. O'Toole,et al.  A generalized autoassociator model for face processing and sex categorization: From principal compon , 1996 .

[168]  Tzyy-Ping Jung,et al.  Independent Component Analysis of Electroencephalographic Data , 1995, NIPS.

[169]  T. Poggio,et al.  Recognition and Structure from one 2D Model View: Observations on Prototypes, Object Classes and Symmetries , 1992 .

[170]  Eric Maël.  A Hierarchial Network for Learning Robust Models of Kinematic Chains , 1996, ICANN.

[171]  Shimon Edelman,et al.  Learning to Recognize Faces from Examples , 1992, ECCV.

[172]  David Beymer,et al.  Face recognition under varying pose , 1994, 1994 Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.

[173]  Harry Wechsler,et al.  Face recognition using hybrid classifiers , 1997, Pattern Recognit..

[174]  E. Oja.  Simplified neuron model as a principal component analyzer , 1982, Journal of mathematical biology.