Copula Ordinal Regression Framework for Joint Estimation of Facial Action Unit Intensity
暂无分享,去创建一个
Vladimir Pavlovic | Maja Pantic | Ognjen Rudovic | Robert Walecki | V. Pavlovic | M. Pantic | Ognjen Rudovic | R. Walecki
[1] T. Louis,et al. Inferences on the association parameter in copula models for bivariate survival data. , 1995, Biometrics.
[2] Maja Pantic,et al. A Dynamic Appearance Descriptor Approach to Facial Actions Temporal Modeling , 2014, IEEE Transactions on Cybernetics.
[3] Qiang Ji,et al. Multiple-Facial Action Unit Recognition by Shared Feature Learning and Semantic Relation Modeling , 2014, 2014 22nd International Conference on Pattern Recognition.
[4] Mohamed Chetouani,et al. Facial Action Unit intensity prediction via Hard Multi-Task Metric Learning for Kernel Regression , 2015, 2015 11th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition (FG).
[5] Qiang Ji,et al. Capturing Global Semantic Relationships for Facial Action Unit Recognition , 2013, 2013 IEEE International Conference on Computer Vision.
[6] Stefanos Zafeiriou,et al. Markov Random Field Structures for Facial Action Unit Intensity Estimation , 2013, 2013 IEEE International Conference on Computer Vision Workshops.
[7] Xiaogang Wang,et al. Deep Learning Face Representation from Predicting 10,000 Classes , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.
[8] Carl E. Rasmussen,et al. Gaussian processes for machine learning , 2005, Adaptive computation and machine learning.
[9] Frank D. Wood,et al. Characterizing neural dependencies with copula models , 2008, NIPS.
[10] ZhouZhi-Hua,et al. Multilabel Neural Networks with Applications to Functional Genomics and Text Categorization , 2006 .
[11] Maja Pantic,et al. Meta-Analysis of the First Facial Expression Recognition Challenge , 2012, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).
[12] H. Friedl. Econometric Analysis of Count Data , 2002 .
[13] Maja Pantic,et al. Doubly Sparse Relevance Vector Machine for Continuous Facial Behavior Estimation , 2016, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[14] Fernando De la Torre,et al. Continuous AU intensity estimation using localized, sparse facial feature space , 2013, 2013 10th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition (FG).
[15] Maja Pantic,et al. Multi-conditional Latent Variable Model for Joint Facial Action Unit Detection , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).
[16] Andrew Zisserman,et al. Deep Face Recognition , 2015, BMVC.
[17] Arman Savran,et al. Regression-based intensity estimation of facial action units , 2012, Image Vis. Comput..
[18] Honggang Zhang,et al. Deep Region and Multi-label Learning for Facial Action Unit Detection , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[19] Maja Pantic,et al. Parametric temporal alignment for the detection of facial action temporal segments , 2014, BMVC.
[20] P. Ekman,et al. Facial action coding system , 2019 .
[21] J. Fleiss,et al. Intraclass correlations: uses in assessing rater reliability. , 1979, Psychological bulletin.
[22] Shiguang Shan,et al. AU-aware Deep Networks for facial expression recognition , 2013, 2013 10th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition (FG).
[23] Dumitru Erhan,et al. Deep Neural Networks for Object Detection , 2013, NIPS.
[24] Grigorios Tsoumakas,et al. Multi-Label Classification: An Overview , 2007, Int. J. Data Warehous. Min..
[25] Guosheng Lin,et al. Efficient Piecewise Training of Deep Structured Models for Semantic Segmentation , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[26] Vladimir Pavlovic,et al. Context-Sensitive Dynamic Ordinal Regression for Intensity Estimation of Facial Action Units , 2015, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[27] Daniel McDuff,et al. Exploiting sparsity and co-occurrence structure for action unit recognition , 2015, 2015 11th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition (FG).
[28] C. Genest. Frank's family of bivariate distributions , 1987 .
[29] Trevor J. Hastie,et al. Exact Covariance Thresholding into Connected Components for Large-Scale Graphical Lasso , 2011, J. Mach. Learn. Res..
[30] R. Tibshirani,et al. Sparse inverse covariance estimation with the graphical lasso. , 2008, Biostatistics.
[31] A. Agresti,et al. Analysis of Ordinal Categorical Data. , 1985 .
[32] Di Huang,et al. Local Binary Patterns and Its Application to Facial Image Analysis: A Survey , 2011, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).
[33] Qiang Ji,et al. A Unified Probabilistic Framework for Spontaneous Facial Action Modeling and Understanding , 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[34] Qiang Ji,et al. A unified probabilistic framework for measuring the intensity of spontaneous facial action units , 2013, 2013 10th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition (FG).
[35] C. Genest,et al. A Primer on Copulas for Count Data , 2007, ASTIN Bulletin.
[36] Andrea Cavallaro,et al. Automatic Analysis of Facial Affect: A Survey of Registration, Representation, and Recognition , 2015, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[37] Joost van de Weijer,et al. From Emotions to Action Units with Hidden and Semi-Hidden-Task Learning , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).
[38] Lijun Yin,et al. FERA 2015 - second Facial Expression Recognition and Analysis challenge , 2015, 2015 11th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition (FG).
[39] Thomas S. Huang,et al. Do Deep Neural Networks Learn Facial Action Units When Doing Expression Recognition? , 2015, 2015 IEEE International Conference on Computer Vision Workshop (ICCVW).
[40] Qingshan Liu,et al. Learning active facial patches for expression analysis , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.
[41] Zuheng Ming,et al. Facial Action Units intensity estimation by the fusion of features with multi-kernel Support Vector Machine , 2015, 2015 11th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition (FG).
[42] Jean-Philippe Thiran,et al. Discriminant multi-label manifold embedding for facial Action Unit detection , 2015, 2015 11th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition (FG).
[43] J. Cohn,et al. All Smiles are Not Created Equal: Morphology and Timing of Smiles Perceived as Amused, Polite, and Embarrassed/Nervous , 2009, Journal of nonverbal behavior.
[44] Maja Pantic,et al. Latent trees for estimating intensity of Facial Action Units , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[45] Qiang Ji,et al. Facial Action Unit Recognition by Exploiting Their Dynamic and Semantic Relationships , 2007, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[46] Mohammad H. Mahoor,et al. DISFA: A Spontaneous Facial Action Intensity Database , 2013, IEEE Transactions on Affective Computing.
[47] Maja Pantic,et al. Continuous Pain Intensity Estimation from Facial Expressions , 2012, ISVC.
[48] Mohammad S. Sorower. A Literature Survey on Algorithms for Multi-label Learning , 2010 .
[49] H. Emrah Tasli,et al. Deep learning based FACS Action Unit occurrence and intensity estimation , 2015, 2015 11th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition (FG).
[50] Rama Chellappa,et al. Structure-Preserving Sparse Decomposition for Facial Expression Analysis , 2014, IEEE Transactions on Image Processing.
[51] A. Sklar,et al. Random variables, distribution functions, and copulas---a personal look backward and forward , 1996 .
[52] Joel E. Pessa,et al. Double or bifid zygomaticus major muscle: Anatomy, incidence, and clinical correlation , 1998, Clinical anatomy.
[53] Fernando Pereira,et al. Structured Learning with Approximate Inference , 2007, NIPS.
[54] Daniel S. Messinger,et al. A framework for automated measurement of the intensity of non-posed Facial Action Units , 2009, 2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops.
[55] Zhi-Hua Zhou,et al. Multilabel Neural Networks with Applications to Functional Genomics and Text Categorization , 2006, IEEE Transactions on Knowledge and Data Engineering.
[56] Geoffrey E. Hinton,et al. Learning representations by back-propagating errors , 1986, Nature.
[57] Shou-De Lin,et al. A Ranking-based KNN Approach for Multi-Label Classification , 2012, ACML.
[58] Vladimir Pavlovic,et al. Variable-state latent conditional random fields for facial expression recognition and action unit detection , 2015, 2015 11th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition (FG).
[59] Qiang Ji,et al. Facial action unit recognition under incomplete data based on multi-label learning with missing labels , 2016, Pattern Recognit..
[60] Stefanos Zafeiriou,et al. Facial Action Recognition in 2D and 3D , 2014 .
[61] Michel F. Valstar,et al. Learning to combine local models for facial Action Unit detection , 2015, 2015 11th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition (FG).
[62] S. Horvath. Weighted Network Analysis: Applications in Genomics and Systems Biology , 2011 .
[63] Jeff G. Schneider,et al. A Composite Likelihood View for Multi-Label Classification , 2012, AISTATS.
[64] Klaus Obermayer,et al. Support vector learning for ordinal regression , 1999 .
[65] Vladimir Pavlovic,et al. Copula Ordinal Regression for Joint Estimation of Facial Action Unit Intensity , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[66] Gang Hua,et al. Ordinal Regression with Multiple Output CNN for Age Estimation , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[67] Mohammad H. Mahoor,et al. Facial action unit recognition with sparse representation , 2011, Face and Gesture 2011.
[68] Vladimir Pavlovic,et al. Structured Output Ordinal Regression for Dynamic Facial Emotion Intensity Prediction , 2010, ECCV.
[69] Jeffrey F. Cohn,et al. Painful data: The UNBC-McMaster shoulder pain expression archive database , 2011, Face and Gesture 2011.
[70] Lijun Yin,et al. Static and dynamic 3D facial expression recognition: A comprehensive survey , 2012, Image Vis. Comput..
[71] Honggang Zhang,et al. Joint patch and multi-label learning for facial action unit detection , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[72] Gwen Littlewort,et al. Automatic Recognition of Facial Actions in Spontaneous Expressions , 2006, J. Multim..
[73] Ashish Kapoor,et al. Multimodal affect recognition in learning environments , 2005, ACM Multimedia.
[74] Simon Lucey,et al. Investigating Spontaneous Facial Action Recognition through AAM Representations of the Face , 2007 .
[75] Stefanos Zafeiriou,et al. A dynamic approach to the recognition of 3D facial expressions and their temporal models , 2011, Face and Gesture 2011.
[76] Anton Schwaighofer,et al. Learning Gaussian processes from multiple tasks , 2005, ICML.
[77] Geoffrey E. Hinton,et al. ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.
[78] Andrew Zisserman,et al. Very Deep Convolutional Networks for Large-Scale Image Recognition , 2014, ICLR.
[79] Sridha Sridharan,et al. Automatically Detecting Pain in Video Through Facial Action Units , 2011, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).
[80] Fernando De la Torre,et al. Selective Transfer Machine for Personalized Facial Action Unit Detection , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition.
[81] Andrew McCallum,et al. Piecewise pseudolikelihood for efficient training of conditional random fields , 2007, ICML '07.
[82] Peter Robinson,et al. Cross-dataset learning and person-specific normalisation for automatic Action Unit detection , 2015, 2015 11th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition (FG).
[83] Markov Random Field , 2010, Encyclopedia of Machine Learning.
[84] Andrew McCallum,et al. Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data , 2001, ICML.