Digital image processing (2nd ed.)
Digital image processing (2nd ed.)
Coding, Analysis, Interpretation, and Recognition of Facial Expressions
IEEE Transactions on Pattern Analysis and Machine Intelligence
A morphable model for the synthesis of 3D faces
Proceedings of the 26th annual conference on Computer graphics and interactive techniques
IEEE Transactions on Pattern Analysis and Machine Intelligence
Automatic Classification of Single Facial Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Recognizing Action Units for Facial Expression Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Building 3-D Human Face Models from Two Photographs
Journal of VLSI Signal Processing Systems - Special issue on multimedia signal processing
IEEE Transactions on Pattern Analysis and Machine Intelligence
Facial Expression Space Learning
PG '02 Proceedings of the 10th Pacific Conference on Computer Graphics and Applications
Model-Based Face Tracking for View-Independent Facial Expression Recognition
FGR '02 Proceedings of the Fifth IEEE International Conference on Automatic Face and Gesture Recognition
Robust Full-Motion Recovery of Head by Dynamic Templates and Re-Registration Techniques
FGR '02 Proceedings of the Fifth IEEE International Conference on Automatic Face and Gesture Recognition
Facial Expression Decomposition
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Facial expression recognition from video sequences: temporal and static modeling
Computer Vision and Image Understanding - Special issue on Face recognition
Real time facial expression recognition in video using support vector machines
Proceedings of the 5th international conference on Multimodal interfaces
Robust Real-Time Face Detection
International Journal of Computer Vision
Active Appearance Models Revisited
International Journal of Computer Vision
Multi-View AAM Fitting and Camera Calibration
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
Generalized Discriminant Analysis Using a Kernel Approach
Neural Computation
A Real-Time Facial Expression Recognition using the STAAM
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 01
An iterative image registration technique with an application to stereo vision
IJCAI'81 Proceedings of the 7th international joint conference on Artificial intelligence - Volume 2
Real-time combined 2D+3D active appearance models
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Coupled Gaussian process regression for pose-invariant facial expression recognition
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part II
Facial expression recognition using tracked facial actions: Classifier performance analysis
Engineering Applications of Artificial Intelligence
3D Virtual worlds and the metaverse: Current status and future possibilities
ACM Computing Surveys (CSUR)
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This paper proposes a real-time person independent facial expression recognition in two parts: one is a model fitting part using a proposed stereo active appearance model (STAAM) and another is a person independent facial expression recognition using a layered generalized discriminant analysis (GDA) classifier. The STAAM fitting algorithm uses multiple calibrated perspective cameras to compute the 3D shape and rigid motion parameters. The use of calibration information reduces the number of model parameters, restricts the degree of freedom in the model parameters, and increases the accuracy and speed of fitting. The STAAM uses a modified simultaneous update fitting method that reduces the fitting computation greatly. Also, the layered GDA classifier combines 3D shape and 2D appearance to improve the recognition performance of person independent facial expressions. Experimental results show that (1) the STAAM shows a better fitting stability than the existing multiple-view AAM (MVAAM), (2) the modified simultaneous update algorithm accelerates the AAM fitting speed, and (3) the combination of the 3D shape and 2D appearance features using a layered GDA classifier improves the performance of facial expression recognition greatly.