Neural Computation
Robust Real-Time Face Pose and Facial Expression Recovery
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
3D Facial Expression Recognition Based on Primitive Surface Feature Distribution
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
A Survey of Affect Recognition Methods: Audio, Visual, and Spontaneous Expressions
IEEE Transactions on Pattern Analysis and Machine Intelligence
Facial Expression Recognition Based on 3D Dynamic Range Model Sequences
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part II
Head Pose Estimation in Computer Vision: A Survey
IEEE Transactions on Pattern Analysis and Machine Intelligence
Pose-Invariant Facial Expression Recognition Using Variable-Intensity Templates
International Journal of Computer Vision
Real-time facial expression recognition using STAAM and layered GDA classifier
Image and Vision Computing
Social signal processing: Survey of an emerging domain
Image and Vision Computing
Image and Vision Computing
Automatic 3d facial expression analysis in videos
AMFG'05 Proceedings of the Second international conference on Analysis and Modelling of Faces and Gestures
Locally Linear Regression for Pose-Invariant Face Recognition
IEEE Transactions on Image Processing
Unsupervised temporal segmentation of talking faces using visual cues to improve emotion recognition
ACII'11 Proceedings of the 4th international conference on Affective computing and intelligent interaction - Volume Part I
3D shape estimation in video sequences provides high precision evaluation of facial expressions
Image and Vision Computing
Multi-view facial expression recognition analysis with generic sparse coding feature
ECCV'12 Proceedings of the 12th international conference on Computer Vision - Volume Part III
A review of motion analysis methods for human Nonverbal Communication Computing
Image and Vision Computing
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We present a novel framework for the recognition of facial expressions at arbitrary poses that is based on 2D geometric features. We address the problem by first mapping the 2D locations of landmark points of facial expressions in non-frontal poses to the corresponding locations in the frontal pose. Then, recognition of the expressions is performed by using any state-of-the-art facial expression recognition method (in our case, multi-class SVM). To learn the mappings that achieve pose normalization, we use a novel Gaussian Process Regression (GPR) model which we name Coupled Gaussian Process Regression (CGPR) model. Instead of learning single GPR model for all target pairs of poses at once, or learning one GPR model per target pair of poses independently of other pairs of poses, we propose CGPR model, which also models the couplings between the GPR models learned independently per target pairs of poses. To the best of our knowledge, the proposed method is the first one satisfying all: (i) being face-shape-model-free, (ii) handling expressive faces in the range from -45° to +45° pan rotation and from -30° to +30° tilt rotation, and (iii) performing accurately for continuous head pose despite the fact that the training was conducted only on a set of discrete poses.