Machine Learning in Computer Vision

Foreword. Preface 1. INTRODUCTION. 1 Research Issues on Learning in Computer Vision. 2 Overview of the Book. 3 Contributions. 2. THEORY: PROBABILISTIC CLASSIFIERS. 1 Introduction. 2 Preliminaries and Notations. 3 Bayes Optimal Error and Entropy. 4 Analysis of Classification Error of Estimated (Mismatched)Distribution. 5 Density of Distributions. 6 Complex Probabilistic Models and Small Sample Effects. 7 Summary. 3. THEORY: GENERALIZATION BOUNDS. 1 Introduction. 2 Preliminaries. 3 A Margin Distribution Based Bound. 4 Analysis. 5 Summary. 4. THEORY: SEMI-SUPERVISED LEARNING. 1 Introduction.2 Properties of Classification. 3 Existing Literature. 4 Semi-supervised Learning Using Maximum Likelihood Estimation. 5 Asymptotic Properties of Maximum Likelihood Estimation with Labeled and Unlabeled Data. 6 Learning with Finite Data. 7 Concluding Remarks. 5. ALGORITHM: MAXIMUM LIKELIHOOD MINIMUM ENTROPY HMM. 1 Previous Work. 2 Mutual Information, Bayes Optimal Error, Entropy, and Conditional Probability. 3 Maximum Mutual Information HMMs. 4 Discussion. 5 Experimental Results. 6 Summary. 6. ALGORITHM: MARGIN DISTRIBUTION OPTIMIZATION. 1 Introduction. 2 A Margin Distribution Based Bound. 3 Existing Learning Algorithms. 4 The Margin Distribution Optimization (MDO) Algorithm. 5 Experimental Evaluation. 6 Conclusions. 7. ALGORITHM: LEARNING THE STRUCTURE OF BAYESIAN NETWORK CLASSIFIERS. 1 Introduction. 2 Bayesian Network Classifiers. 3 Switching between Models: Naive Bayes and TAN Classifiers. 4 Learning the Structure of Bayesian Network Classifiers: Existing Approaches. 5 Classification Driven Stochastic Structure Search. 6 Experiments. 7 Should Unlabeled Data Be Weighed Differently? 8 Active Learning. 9 Concluding Remarks. 8. APPLICATION: OFFICE ACTIVITY RECOGNITION. 1 Context-Sensitive Systems. 2 Towards Tractable and Robust Context Sensing. 3 Layered Hidden Markov Models (LHMMs). 4 Implementation of SEER. 5 Experiments. 6 Related Representations. 7Summary. 9. APPLICATION: MULTIMODAL EVENT DETECTION. 1 Fusion Models: A Review. 2 A Hierarchical Fusion Model. 3 Experimental Setup, Features, and Results. 4 Summary. 10. APPLICATION: FACIAL EXPRESSION RECOGNITION. 1 Introduction. 2 Human Emotion Research. 3 Facial Expression Recognition System. 4 Experimental Analysis. 5 Discussion. 11. APPLICATION: BAYESIAN NETWORK CLASSIFIERS FOR FACE DETECTION. 1 Introduction. 2 Related Work. 3 Applying Bayesian Network Classifiers to Face Detection. 4 Experiments. 5 Discussion. References. Index.

[1]  Andrew R. Golding,et al.  A Bayesian Hybrid Method for Context-sensitive Spelling Correction , 1996, VLC@ACL.

[2]  J. Lien,et al.  Automatic recognition of facial expressions using hidden markov models and estimation of expression intensity , 1998 .

[3]  Nicu Sebe,et al.  How to complete performance graphs in content-based image retrieval: add generality and normalize scope , 2005, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[4]  H. Schlosberg.  Three dimensions of emotion. , 1954, Psychological review.

[5]  Tom Burr,et al.  Causation, Prediction, and Search , 2003, Technometrics.

[6]  Walter F. Bischof,et al.  Learning spatio-temporal relational structures , 2001, Appl. Artif. Intell..

[7]  R. Chhikara,et al.  Linear discriminant analysis with misallocation in training samples , 1984 .

[8]  Monson H. Hayes,et al.  An embedded HMM-based approach for face detection and recognition , 1999, 1999 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings. ICASSP99 (Cat. No.99CH36258).

[9]  Gareth James,et al.  Variance and Bias for General Loss Functions , 2003, Machine Learning.

[10]  Hong Yan,et al.  Fast algorithm for locating head boundaries , 1994, J. Electronic Imaging.

[11]  Dan Roth,et al.  Learning in Natural Language , 1999, IJCAI.

[12]  Larry S. Davis,et al.  Human expression recognition from motion using a radial basis function network architecture , 1996, IEEE Trans. Neural Networks.

[13]  S. Kullback.  Probability Densities with Given Marginals , 1968 .

[14]  Lawrence Sirovich,et al.  Application of the Karhunen-Loeve Procedure for the Characterization of Human Faces , 1990, IEEE Trans. Pattern Anal. Mach. Intell..

[15]  K. Oatley,et al.  Human emotions : a reader , 1998 .

[16]  Dan Roth,et al.  Learning Coherent Concepts , 2001, ALT.

[17]  Ryszard S. Michalski,et al.  A Theory and Methodology of Inductive Learning , 1983, Artificial Intelligence.

[18]  Timothy F. Cootes,et al.  Learning to identify and track faces in image sequences , 1998, Sixth International Conference on Computer Vision (IEEE Cat. No.98CH36271).

[19]  Alexander H. Waibel,et al.  A real-time face tracker , 1996, Proceedings Third IEEE Workshop on Applications of Computer Vision. WACV'96.

[20]  Aaron F. Bobick,et al.  A Framework for Recognizing Multi-Agent Action from Visual Evidence , 1999, AAAI/IAAI.

[21]  P. Lang.  The emotion probe. Studies of motivation and attention. , 1995, The American psychologist.

[22]  L. Vistnes.  The Artist??s Complete Guide to Facial Expression , 1992 .

[23]  David H. Wolpert,et al.  Stacked generalization , 1992, Neural Networks.

[24]  Alex Pentland,et al.  Unsupervised clustering of ambulatory audio and video , 1999, 1999 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings. ICASSP99 (Cat. No.99CH36258).

[25]  Judea Pearl,et al.  Probabilistic reasoning in intelligent systems - networks of plausible inference , 1991, Morgan Kaufmann series in representation and reasoning.

[26]  W. B. Johnson,et al.  Extensions of Lipschitz mappings into Hilbert space , 1984 .

[27]  María G. Cisneros-Solís,et al.  MEDICAL ANNUAL , 1958, Journal of The Royal Naval Medical Service.

[28]  Kamal Nigamyknigam,et al.  Employing Em in Pool-based Active Learning for Text Classiication , 1998 .

[29]  A. Martinez,et al.  Face image retrieval using HMMs , 1999, Proceedings IEEE Workshop on Content-Based Access of Image and Video Libraries (CBAIVL'99).

[30]  Nicu Sebe,et al.  Emotion recognition using a Cauchy Naive Bayes classifier , 2002, Object recognition supported by user interaction for service robots.

[31]  Beat Fasel,et al.  Automati Fa ial Expression Analysis: A Survey , 1999 .

[32]  Takeo Kanade,et al.  Probabilistic modeling of local appearance and spatial relationships for object recognition , 1998, Proceedings. 1998 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No.98CB36231).

[33]  Weiru Liu,et al.  Learning belief networks from data: an information theory based approach , 1997, CIKM '97.

[34]  Biing-Hwang Juang,et al.  Fundamentals of speech recognition , 1993, Prentice Hall signal processing series.

[35]  R. Redner,et al.  Mixture densities, maximum likelihood, and the EM algorithm , 1984 .

[36]  Ron Kohavi,et al.  Supervised and Unsupervised Discretization of Continuous Features , 1995, ICML.

[37]  Dana Ron,et al.  An Experimental and Theoretical Comparison of Model Selection Methods , 1995, COLT '95.

[38]  Lawrence R. Rabiner,et al.  A tutorial on hidden Markov models and selected applications in speech recognition , 1989, Proc. IEEE.

[39]  Jay G. Wilpon,et al.  Modeling state durations in hidden Markov models for automatic speech recognition , 1992, [Proceedings] ICASSP-92: 1992 IEEE International Conference on Acoustics, Speech, and Signal Processing.

[40]  Bin Shen,et al.  Structural Extension to Logistic Regression: Discriminative Parameter Learning of Belief Net Classifiers , 2002, Machine Learning.

[41]  Catherine Blake,et al.  UCI Repository of machine learning databases , 1998 .

[42]  Piotr Indyk,et al.  Algorithmic applications of low-distortion geometric embeddings , 2001, Proceedings 2001 IEEE International Conference on Cluster Computing.

[43]  Neri Merhav,et al.  Relations between entropy and error probability , 1994, IEEE Trans. Inf. Theory.

[44]  Ayhan Demiriz,et al.  Semi-Supervised Support Vector Machines , 1998, NIPS.

[45]  Takeo Kanade,et al.  Neural Network-Based Face Detection , 1998, IEEE Trans. Pattern Anal. Mach. Intell..

[46]  Ying Dai,et al.  Face-texture model based on SGLD and its application in face detection in a color scene , 1996, Pattern Recognit..

[47]  David J. Miller,et al.  A Mixture of Experts Classifier with Learning Based on Both Labelled and Unlabelled Data , 1996, NIPS.

[48]  T. Cover,et al.  The relative value of labeled and unlabeled samples in pattern recognition , 1993, Proceedings. IEEE International Symposium on Information Theory.

[49]  Michael S. Lew,et al.  Information theoretic view-based and modular face detection , 1996, Proceedings of the Second International Conference on Automatic Face and Gesture Recognition.

[50]  Bruce E. Hajek,et al.  Cooling Schedules for Optimal Annealing , 1988, Math. Oper. Res..

[51]  Abbas Z. Kouzani,et al.  Locating human faces within images , 2003, Comput. Vis. Image Underst..

[52]  Robert E. Schapire,et al.  Efficient distribution-free learning of probabilistic concepts , 1990, Proceedings [1990] 31st Annual Symposium on Foundations of Computer Science.

[53]  Takeo Kanade,et al.  Comprehensive database for facial expression analysis , 2000, Proceedings Fourth IEEE International Conference on Automatic Face and Gesture Recognition (Cat. No. PR00580).

[54]  Thomas S. Huang,et al.  Connected vibrations: a modal analysis approach for non-rigid motion tracking , 1998, Proceedings. 1998 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No.98CB36231).

[55]  David A. Landgrebe,et al.  The effect of unlabeled samples in reducing the small sample size problem and mitigating the Hughes phenomenon , 1994, IEEE Trans. Geosci. Remote. Sens..

[56]  Sankar K. Pal,et al.  Pattern Recognition: From Classical to Modern Approaches , 2001 .

[57]  Vladimir Vapnik,et al.  Statistical learning theory , 1998 .

[58]  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.

[59]  Vittorio Castelli,et al.  On the exponential value of labeled samples , 1995, Pattern Recognit. Lett..

[60]  C. N. Liu,et al.  Approximating discrete probability distributions with dependence trees , 1968, IEEE Trans. Inf. Theory.

[61]  Richard O. Duda,et al.  Pattern classification and scene analysis , 1974, A Wiley-Interscience publication.

[62]  Nicu Sebe,et al.  Learning Bayesian network classifiers for facial expression recognition both labeled and unlabeled data , 2003, 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003. Proceedings..

[63]  Paul A. Viola,et al.  Robust Real-time Object Detection , 2001 .

[64]  Nir Friedman,et al.  The Bayesian Structural EM Algorithm , 1998, UAI.

[65]  Milind R. Naphade,et al.  Stochastic modeling of soundtrack for efficient segmentation and indexing of video , 1999, Electronic Imaging.

[66]  J. N. Bassili.  Emotion recognition: the role of facial movement and the relative importance of upper and lower areas of the face. , 1979, Journal of personality and social psychology.

[67]  Santosh S. Vempala,et al.  An algorithmic theory of learning: Robust concepts and random projection , 1999, Machine Learning.

[68]  Alex Pentland,et al.  Real-Time American Sign Language Recognition Using Desk and Wearable Computer Based Video , 1998, IEEE Trans. Pattern Anal. Mach. Intell..

[69]  Nir Friedman,et al.  Bayesian Network Classifiers , 1997, Machine Learning.

[70]  Rémi Gilleron,et al.  Positive and Unlabeled Examples Help Learning , 1999, ALT.

[71]  K. D. Jong.  Learning with Genetic Algorithms: An Overview , 2005, Machine Learning.

[72]  J. Aggarwal,et al.  A Bayesian approach to human activity recognition , 1999, Proceedings Second IEEE Workshop on Visual Surveillance (VS'99) (Cat. No.98-89223).

[73]  Maja Pantic,et al.  Automatic Analysis of Facial Expressions: The State of the Art , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[74]  A P Dawid,et al.  Properties of diagnostic data distributions. , 1976, Biometrics.

[75]  R. Gray.  Entropy and Information Theory , 1990, Springer New York.

[76]  H. White.  Maximum Likelihood Estimation of Misspecified Models , 1982 .

[77]  Mahesh Viswanathan,et al.  A prototype document image analysis system for technical journals , 1992, Computer.

[78]  Gregory F. Cooper,et al.  A Bayesian method for the induction of probabilistic networks from data , 1992, Machine Learning.

[79]  Michael C. Burl,et al.  Finding faces in cluttered scenes using random labeled graph matching , 1995, Proceedings of IEEE International Conference on Computer Vision.

[80]  Robert M. Haralick,et al.  The Consistent Labeling Problem: Part I , 1979, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[81]  Lawrence S. Chen,et al.  Joint processing of audio-visual information for the recognition of emotional expressions in human-computer interaction , 2000 .

[82]  Stuart J. Russell,et al.  The BATmobile: Towards a Bayesian Automated Taxi , 1995, IJCAI.

[83]  Dan Roth,et al.  Understanding Probabilistic Classifiers , 2001, ECML.

[84]  Nicu Sebe,et al.  Facial expression recognition from video sequences , 2002, Proceedings. IEEE International Conference on Multimedia and Expo.

[85]  N. Metropolis,et al.  Equation of State Calculations by Fast Computing Machines , 1953, Resonance.

[86]  Terence J. O'Neill.  Normal Discrimination with Unclassified Observations , 1978 .

[87]  Uday B. Desai,et al.  Finding faces in photographs , 1998, Sixth International Conference on Computer Vision (IEEE Cat. No.98CH36271).

[88]  Jesse Hoey,et al.  Hierarchical unsupervised learning of facial expression categories , 2001, Proceedings IEEE Workshop on Detection and Recognition of Events in Video.

[89]  Shaogang Gong,et al.  Modelling facial colour and identity with Gaussian mixtures , 1998, Pattern Recognit..

[90]  Santosh S. Venkatesh,et al.  Learning from a mixture of labeled and unlabeled examples with parametric side information , 1995, COLT '95.

[91]  D. Spalding.  The Principles of Psychology , 1873, Nature.

[92]  P. Ekman,et al.  Strong evidence for universals in facial expressions: a reply to Russell's mistaken critique. , 1994, Psychological bulletin.

[93]  Tsuhan Chen,et al.  Audio-visual integration in multimodal communication , 1998, Proc. IEEE.

[94]  Nicu Sebe,et al.  Facial expression recognition from video sequences: temporal and static modeling , 2003, Comput. Vis. Image Underst..

[95]  Takeo Kanade,et al.  Rotation invariant neural network-based face detection , 1998, Proceedings. 1998 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No.98CB36231).

[96]  John R. Kender,et al.  Finding skin in color images , 1996, Proceedings of the Second International Conference on Automatic Face and Gesture Recognition.

[97]  John Shawe-Taylor,et al.  Classification Accuracy Based on Observed Margin , 1998, Algorithmica.

[98]  Thomas S. Huang,et al.  Face detection with information-based maximum discrimination , 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[99]  Roberto Cipolla,et al.  Feature-based human face detection , 1997, Image Vis. Comput..

[100]  Rayid Ghani,et al.  Combining Labeled and Unlabeled Data for MultiClass Text Categorization , 2002, ICML.

[101]  Tom Michael Mitchell,et al.  The Role of Unlabeled Data in Supervised Learning , 2004 .

[102]  László Györfi,et al.  A Probabilistic Theory of Pattern Recognition , 1996, Stochastic Modelling and Applied Probability.

[103]  Thomas S. Huang,et al.  Human face detection in a complex background , 1994, Pattern Recognit..

[104]  D. Hosmer.  A Comparison of Iterative Maximum Likelihood Estimates of the Parameters of a Mixture of Two Normal Distributions Under Three Different Types of Sample , 1973 .

[105]  G. McLachlan,et al.  The efficiency of a linear discriminant function based on unclassified initial samples , 1978 .

[106]  Nicu Sebe,et al.  Evaluation of Expression Recognition Techniques , 2003, CIVR.

[107]  Dariu Gavrila,et al.  Looking at people , 2007, AVSS.

[108]  Dan Roth,et al.  A Winnow-Based Approach to Context-Sensitive Spelling Correction , 1998, Machine Learning.

[109]  Yoram Singer,et al.  Improved Boosting Algorithms Using Confidence-rated Predictions , 1998, COLT' 98.

[110]  David B. Cooper,et al.  On the Asymptotic Improvement in the Out- come of Supervised Learning Provided by Additional Nonsupervised Learning , 1970, IEEE Transactions on Computers.

[111]  Hendrik Blockeel,et al.  Top-Down Induction of First Order Logical Decision Trees , 1998, AI Commun..

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

[113]  Ron Kohavi,et al.  Scaling Up the Accuracy of Naive-Bayes Classifiers: A Decision-Tree Hybrid , 1996, KDD.

[114]  P. J. Huber.  The behavior of maximum likelihood estimates under nonstandard conditions , 1967 .

[115]  Andrew D. Christian,et al.  Digital smart kiosk project , 1998, CHI.

[116]  Charles Elkan,et al.  Boosting and Naive Bayesian learning , 1997 .

[117]  Eric Horvitz,et al.  Attention-Sensitive Alerting , 1999, UAI.

[118]  Tomaso A. Poggio,et al.  Example-Based Learning for View-Based Human Face Detection , 1998, IEEE Trans. Pattern Anal. Mach. Intell..

[119]  Fabio Gagliardi Cozman,et al.  Unlabeled Data Can Degrade Classification Performance of Generative Classifiers , 2002, FLAIRS.

[120]  Vladimir Pavlovic,et al.  Audio-visual speaker detection using dynamic Bayesian networks , 2000, Proceedings Fourth IEEE International Conference on Automatic Face and Gesture Recognition (Cat. No. PR00580).

[121]  I. J. Myung,et al.  Counting probability distributions: Differential geometry and model selection , 2000, Proc. Natl. Acad. Sci. USA.

[122]  D. Rubin,et al.  Maximum likelihood from incomplete data via the EM - algorithm plus discussions on the paper , 1977 .

[123]  Geoffrey J. McLachlan,et al.  Mixture models : inference and applications to clustering , 1989 .

[124]  Narendra Ahuja,et al.  A SNoW-Based Face Detector , 1999, NIPS.

[125]  Jun Ohya,et al.  Recognizing multiple persons' facial expressions using HMM based on automatic extraction of significant frames from image sequences , 1997, Proceedings of International Conference on Image Processing.

[126]  Anthony G. Cohn,et al.  Building qualitative event models automatically from visual input , 1998, Sixth International Conference on Computer Vision (IEEE Cat. No.98CH36271).

[127]  Takeo Kanade,et al.  Semantic analysis for video contents extraction—spotting by association in news video , 1997, MULTIMEDIA '97.

[128]  Subhas C. Nandy,et al.  Efficiency of discriminant analysis when initial samples are classified stochastically , 1990, Pattern Recognit..

[129]  Aaron F. Bobick,et al.  Recognition and interpretation of parametric gesture , 1998, Sixth International Conference on Computer Vision (IEEE Cat. No.98CH36271).

[130]  Timothy F. Cootes,et al.  An Automatic Face Identification System Using Flexible Appearance Models , 1994, BMVC.

[131]  R. Berk,et al.  Limiting Behavior of Posterior Distributions when the Model is Incorrect , 1966 .

[132]  Alex Pentland,et al.  Coding, Analysis, Interpretation, and Recognition of Facial Expressions , 1997, IEEE Trans. Pattern Anal. Mach. Intell..

[133]  Nicu Sebe,et al.  Robust Computer Vision: Theory and Applications , 2003 .

[134]  Stuart J. Russell,et al.  Adaptive Probabilistic Networks with Hidden Variables , 1997, Machine Learning.

[135]  Kenji Mase,et al.  Recognition of Facial Expression from Optical Flow , 1991 .

[136]  David Matsumoto,et al.  Cultural Influences on Judgments of Facial Expressions of Emotion (特集テーマ・顔・表情・ジェスチャの認識・合成) -- (表情) , 1999 .

[137]  Henry Stark,et al.  Probability, Random Processes, and Estimation Theory for Engineers , 1995 .

[138]  Åsa Rudström,et al.  Applications of Machine Learning , 2020, Algorithms for Intelligent Systems.

[139]  Tong Zhang,et al.  The Value of Unlabeled Data for Classification Problems , 2000, ICML 2000.

[140]  C. Darwin.  The Expression of the Emotions in Man and Animals , .

[141]  Milind R. Naphade,et al.  Multimodal pattern matching for audio-visual query and retrieval , 2001, IS&T/SPIE Electronic Imaging.

[142]  Michael I. Jordan,et al.  Factorial Hidden Markov Models , 1995, Machine Learning.

[143]  Brendan J. Frey,et al.  Probabilistic multimedia objects (multijects): a novel approach to video indexing and retrieval in multimedia systems , 1998, Proceedings 1998 International Conference on Image Processing. ICIP98 (Cat. No.98CB36269).

[144]  Milind R. Naphade,et al.  Semantic video indexing using a probabilistic framework , 2000, Proceedings 15th International Conference on Pattern Recognition. ICPR-2000.

[145]  P. Ekman.  Emotion in the human face , 1982 .

[146]  Trevor Darrell,et al.  Integrated Person Tracking Using Stereo, Color, and Pattern Detection , 2000, International Journal of Computer Vision.

[147]  S. Demleitner.  [Communication without words]. , 1997, Pflege aktuell.

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

[149]  Dan Roth,et al.  Learning to Resolve Natural Language Ambiguities: A Unified Approach , 1998, AAAI/IAAI.

[150]  Shigeo Morishima,et al.  Expression analysis/synthesis system based on emotion space constructed by multilayered neural network , 1994 .

[151]  Vittorio Castelli,et al.  The relative value of labeled and unlabeled samples in pattern recognition with an unknown mixing parameter , 1996, IEEE Trans. Inf. Theory.

[152]  Yoram Singer,et al.  The Hierarchical Hidden Markov Model: Analysis and Applications , 1998, Machine Learning.

[153]  Alex Pentland,et al.  Coupled hidden Markov models for complex action recognition , 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[154]  Russell Greiner,et al.  Model Selection Criteria for Learning Belief Nets: An Empirical Comparison , 2000, ICML.

[155]  V. Vapnik.  Estimation of Dependences Based on Empirical Data , 2006 .

[156]  Thore Graepel,et al.  A PAC-Bayesian Margin Bound for Linear Classifiers: Why SVMs work , 2000, NIPS.

[157]  Aaron F. Bobick,et al.  Recognition of Visual Activities and Interactions by Stochastic Parsing , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[158]  Sebastian Thrun,et al.  Text Classification from Labeled and Unlabeled Documents using EM , 2000, Machine Learning.

[159]  P. Lachenbruch,et al.  Discriminant Analysis When Scale Contamination Is Present in the Initial Sample , 1977 .

[160]  Michael S. Brandstein,et al.  A practical methodology for speech source localization with microphone arrays , 1997, Comput. Speech Lang..

[161]  Eric Horvitz,et al.  The Lumière Project: Bayesian User Modeling for Inferring the Goals and Needs of Software Users , 1998, UAI.

[162]  Naftali Tishby,et al.  The information bottleneck method , 2000, ArXiv.

[163]  Vladimir Pavlovic,et al.  Boosted learning in dynamic Bayesian networks for multimodal speaker detection , 2003, Proc. IEEE.

[164]  David Haussler,et al.  Learnability and the Vapnik-Chervonenkis dimension , 1989, JACM.

[165]  James L. Crowley,et al.  Multi-modal tracking of faces for video communications , 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[166]  J. Pearl.  Causality: Models, Reasoning and Inference , 2000 .

[167]  Takeo Kanade,et al.  A statistical method for 3D object detection applied to faces and cars , 2000, Proceedings IEEE Conference on Computer Vision and Pattern Recognition. CVPR 2000 (Cat. No.PR00662).

[168]  Shumeet Baluja,et al.  Probabilistic Modeling for Face Orientation Discrimination: Learning from Labeled and Unlabeled Data , 1998, NIPS.

[169]  Michael J. Black,et al.  Tracking and recognizing rigid and non-rigid facial motions using local parametric models of image motion , 1995, Proceedings of IEEE International Conference on Computer Vision.

[170]  David A. Landgrebe,et al.  Classification of multispectral data by joint supervised-unsupervised learning , 1993 .

[171]  Leslie G. Valiant,et al.  A theory of the learnable , 1984, STOC '84.

[172]  Yoram Singer,et al.  Unsupervised Models for Named Entity Classification , 1999, EMNLP.

[173]  Pedro M. Domingos,et al.  Beyond Independence: Conditions for the Optimality of the Simple Bayesian Classifier , 1996, ICML.

[174]  Ian Craw,et al.  Finding Face Features , 1992, ECCV.

[175]  Matthew Brand,et al.  An Entropic Estimator for Structure Discovery , 1998, NIPS.

[176]  Lalit R. Bahl,et al.  Estimating hidden Markov model parameters so as to maximize speech recognition accuracy , 1993, IEEE Trans. Speech Audio Process..

[177]  J. Cacioppo,et al.  Inferring psychological significance from physiological signals. , 1990, The American psychologist.

[178]  L. Rothkrantz,et al.  Toward an affect-sensitive multimodal human-computer interaction , 2003, Proc. IEEE.

[179]  Alex Pentland,et al.  LAFTER: a real-time face and lips tracker with facial expression recognition , 2000, Pattern Recognit..

[180]  Matthew Brand,et al.  Discovery and Segmentation of Activities in Video , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[181]  J. York,et al.  Bayesian Graphical Models for Discrete Data , 1995 .

[182]  Thomas M. Cover,et al.  Elements of Information Theory , 2005 .

[183]  Erik Hjelmås,et al.  Face Detection: A Survey , 2001, Comput. Vis. Image Underst..

[184]  Tom M. Mitchell,et al.  Using unlabeled data to improve text classification , 2001 .

[185]  Avrim Blum,et al.  The Bottleneck , 2021, Monopsony Capitalism.

[186]  Alex Pentland,et al.  Maximum Conditional Likelihood via Bound Maximization and the CEM Algorithm , 1998, NIPS.

[187]  Kevin P. Murphy,et al.  Linear-time inference in Hierarchical HMMs , 2001, NIPS.

[188]  C. Izard.  Innate and universal facial expressions: evidence from developmental and cross-cultural research. , 1994, Psychological bulletin.

[189]  Christine L. Lisetti,et al.  Modeling Multimodal Expression of User’s Affective Subjective Experience , 2002, User Modeling and User-Adapted Interaction.

[190]  Nello Cristianini,et al.  An introduction to Support Vector Machines , 2000 .

[191]  F ROSENBLATT,et al.  The perceptron: a probabilistic model for information storage and organization in the brain. , 1958, Psychological review.

[192]  Thomas S. Huang,et al.  Generative and discriminative face modelling for detection , 2002, Proceedings of Fifth IEEE International Conference on Automatic Face Gesture Recognition.

[193]  H. Buxton,et al.  Advanced visual surveillance using Bayesian networks , 1997 .

[194]  Larry S. Davis,et al.  Recognizing Human Facial Expressions From Long Image Sequences Using Optical Flow , 1996, IEEE Trans. Pattern Anal. Mach. Intell..

[195]  Michael I. Jordan,et al.  Hidden Markov Decision Trees , 1996, NIPS.

[196]  Saul Greenberg,et al.  Judging people's availability for interaction from video snapshots , 1999, Proceedings of the 32nd Annual Hawaii International Conference on Systems Sciences. 1999. HICSS-32. Abstracts and CD-ROM of Full Papers.

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

[198]  Narendra Ahuja,et al.  Detecting Faces in Images: A Survey , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[199]  Anastasios Tefas,et al.  Variants of dynamic link architecture based on mathematical morphology for frontal face authentication , 1998, Proceedings. 1998 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No.98CB36231).

[200]  John R. Anderson,et al.  MACHINE LEARNING An Artificial Intelligence Approach , 2009 .

[201]  Dimitris Achlioptas,et al.  Database-friendly random projections , 2001, PODS.

[202]  Nir Friedman,et al.  Being Bayesian About Network Structure. A Bayesian Approach to Structure Discovery in Bayesian Networks , 2004, Machine Learning.

[203]  Yan Zhou,et al.  Enhancing Supervised Learning with Unlabeled Data , 2000, ICML.

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

[205]  M. Kenward,et al.  An Introduction to the Bootstrap , 2007 .

[206]  Monson H. Hayes,et al.  Face Recognition Using An Embedded HMM , 1999 .

[207]  Nicu Sebe,et al.  Authentic facial expression analysis , 2004, Sixth IEEE International Conference on Automatic Face and Gesture Recognition, 2004. Proceedings..

[208]  Ramakant Nevatia,et al.  Representation and optimal recognition of human activities , 2000, Proceedings IEEE Conference on Computer Vision and Pattern Recognition. CVPR 2000 (Cat. No.PR00662).

[209]  Yoav Freund,et al.  Boosting the margin: A new explanation for the effectiveness of voting methods , 1997, ICML.

[210]  David C. Hogg,et al.  Learning Variable-Length Markov Models of Behavior , 2001, Comput. Vis. Image Underst..

[211]  Nello Cristianini,et al.  Further results on the margin distribution , 1999, COLT '99.

[212]  E. Vesterinen,et al.  Affective Computing , 2009, Encyclopedia of Biometrics.

[213]  G. McLachlan.  Discriminant Analysis and Statistical Pattern Recognition , 1992 .