Multi-output Laplacian dynamic ordinal regression for facial expression recognition and intensity estimation
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[1] Shaogang Gong,et al. Dynamic Facial Expression Recognition Using A Bayesian Temporal Manifold Model , 2006, BMVC.
[2] 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.
[3] Wu-Jun Li,et al. Gaussian Process Latent Random Field , 2010, AAAI.
[4] N. Ambady,et al. Thin slices of expressive behavior as predictors of interpersonal consequences: A meta-analysis. , 1992 .
[5] Zhihong Zeng,et al. A Survey of Affect Recognition Methods: Audio, Visual, and Spontaneous Expressions , 2007, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[6] John McDonald,et al. Automatic estimation of the dynamics of facial expression using a three-level model of intensity , 2008, 2008 8th IEEE International Conference on Automatic Face & Gesture Recognition.
[7] Hatice Gunes,et al. Automatic Temporal Segment Detection and Affect Recognition From Face and Body Display , 2009, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).
[8] Qingshan Liu,et al. RankBoost with l1 regularization for facial expression recognition and intensity estimation , 2009, 2009 IEEE 12th International Conference on Computer Vision.
[9] Maja Pantic,et al. Fully Automatic Recognition of the Temporal Phases of Facial Actions , 2012, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).
[10] Arman Savran,et al. Regression-based intensity estimation of facial action units , 2012, Image Vis. Comput..
[11] Wei Chu,et al. New approaches to support vector ordinal regression , 2005, ICML.
[12] Lijun Yin,et al. A high-resolution 3D dynamic facial expression database , 2008, 2008 8th IEEE International Conference on Automatic Face & Gesture Recognition.
[13] Vladimir Pavlovic,et al. Hidden Conditional Ordinal Random Fields for Sequence Classification , 2010, ECML/PKDD.
[14] Andrew McCallum,et al. Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data , 2001, ICML.
[15] Maja Pantic,et al. A Dynamic Texture-Based Approach to Recognition of Facial Actions and Their Temporal Models , 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[16] Trevor Darrell,et al. Conditional Random Fields for Object Recognition , 2004, NIPS.
[17] Vladimir Pavlovic,et al. Structured Output Ordinal Regression for Dynamic Facial Emotion Intensity Prediction , 2010, ECCV.
[18] Shaogang Gong,et al. Appearance Manifold of Facial Expression , 2005, ICCV-HCI.
[19] Zhihong Zeng,et al. A Survey of Affect Recognition Methods: Audio, Visual, and Spontaneous Expressions , 2009, IEEE Trans. Pattern Anal. Mach. Intell..
[20] Takeo Kanade,et al. Detection, tracking, and classification of action units in facial expression , 2000, Robotics Auton. Syst..
[21] Wei Chu,et al. Gaussian Processes for Ordinal Regression , 2005, J. Mach. Learn. Res..
[22] Ashok Samal,et al. Automatic recognition and analysis of human faces and facial expressions: a survey , 1992, Pattern Recognit..
[23] Amnon Shashua,et al. Ranking with Large Margin Principle: Two Approaches , 2002, NIPS.
[24] Maja Pantic,et al. Particle filtering with factorized likelihoods for tracking facial features , 2004, Sixth IEEE International Conference on Automatic Face and Gesture Recognition, 2004. Proceedings..
[25] Xiaofei He,et al. Locality Preserving Projections , 2003, NIPS.
[26] Fadi Dornaika,et al. Real time 3D face and facial feature tracking , 2007, Journal of Real-Time Image Processing.
[27] J. Cohn,et al. Deciphering the Enigmatic Face , 2005, Psychological science.