Active shape models—their training and application
Computer Vision and Image Understanding
Coding, Analysis, Interpretation, and Recognition of Facial Expressions
IEEE Transactions on Pattern Analysis and Machine Intelligence
Recognizing Facial Expressions in Image Sequences Using Local Parameterized Models of Image Motion
International Journal of Computer Vision
Automatic Analysis of Facial Expressions: The State of the Art
IEEE Transactions on Pattern Analysis and Machine Intelligence
Recognizing Action Units for Facial Expression Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Cluster-preserving Embedding of Proteins
Cluster-preserving Embedding of Proteins
Facial expression recognition from video sequences: temporal and static modeling
Computer Vision and Image Understanding - Special issue on Face recognition
Probabilistic recognition of human faces from video
Computer Vision and Image Understanding - Special issue on Face recognition
Separating Style and Content with Bilinear Models
Neural Computation
On bending invariant signatures for surfaces
IEEE Transactions on Pattern Analysis and Machine Intelligence
A survey of affect recognition methods: audio, visual and spontaneous expressions
Proceedings of the 9th international conference on Multimodal interfaces
Pose-Invariant Facial Expression Recognition Using Variable-Intensity Templates
International Journal of Computer Vision
Geometric and Optical Flow Based Method for Facial Expression Recognition in Color Image Sequences
ICCVG 2008 Proceedings of the International Conference on Computer Vision and Graphics: Revised Papers
Pose-invariant facial expression recognition using variable-intensity templates
ACCV'07 Proceedings of the 8th Asian conference on Computer vision - Volume Part I
Image ratio features for facial expression recognition application
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics - Special issue on game theory
Bilinear kernel reduced rank regression for facial expression synthesis
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part II
Facial expression recognition using nonrigid motion parameters and shape-from-shading
CAIP'11 Proceedings of the 14th international conference on Computer analysis of images and patterns - Volume Part II
Facial expressions in American sign language: Tracking and recognition
Pattern Recognition
A realistic dynamic facial expression transfer method
Neurocomputing
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
Dimensionality reduction-based spoken emotion recognition
Multimedia Tools and Applications
Shape classification by manifold learning in multiple observation spaces
Information Sciences: an International Journal
Linear subspaces for facial expression recognition
Image Communication
Hi-index | 0.00 |
We propose a novel approach for modeling, tracking, and recognizing facial expressions on a low-dimensional expression manifold. A modified Lipschitz embedding is developed to embed aligned facial features in a low-dimensional space, while keeping the main structure of the manifold. In the embedded space, a complete expression sequence becomes a path on the expression manifold, emanating from a center that corresponds to the neutral expression. As an offline training stage, facial contour features are first clustered in this space, using a mixture model. For each cluster in the low-dimensional space, a specific ASM model is learned, in order to avoid incorrect matching due to non-linear image variations. A probabilistic model of transitions between the clusters and paths in the embedded space is then learned. Given a new expression sequence, we use ICondensation to track facial features, while recognizing facial expressions simultaneously, within the common probabilistic framework. Experimental results demonstrate that our probabilistic facial expression model on the manifold significantly improves facial deformation tracking and expression recognition. We also synthesize image sequences of changing expressions through the manifold model.