Information-Based Evaluation Criterion for Classifier's Performance
Machine Learning
Using moment invariants and HMM in facial expression recognition
Pattern Recognition Letters
Comprehensive Database for Facial Expression Analysis
FG '00 Proceedings of the Fourth IEEE International Conference on Automatic Face and Gesture Recognition 2000
Facial expression recognition from video sequences: temporal and static modeling
Computer Vision and Image Understanding - Special issue on Face recognition
Manifold Based Analysis of Facial Expression
CVPRW '04 Proceedings of the 2004 Conference on Computer Vision and Pattern Recognition Workshop (CVPRW'04) Volume 5 - Volume 05
Variational Learning for Switching State-Space Models
Neural Computation
Accurate Face Alignment using Shape Constrained Markov Network
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
Recognizing Facial Expressions in Videos Using a Facial Action Analysis-Synthesis Scheme
AVSS '06 Proceedings of the IEEE International Conference on Video and Signal Based Surveillance
Recognising facial expressions in video sequences
Pattern Analysis & Applications
From facial expression to level of interest: a spatio-temporal approach
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
An enhanced independent component-based human facial expression recognition from video
IEEE Transactions on Consumer Electronics
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In this work we examine the use of State-Space Models to model the temporal information of dynamic facial expressions. The later being represented by the 3D animation parameters which are recovered using 3D Candide model. The 3D animation parameters of an image sequence can be seen as the observation of a stochastic process which can be modeled by a linear State-Space Model, the Kalman Filter. In the proposed approach each emotion is represented by a Kalman Filter, with parameters being State Transition matrix, Observation matrix, State and Observation noise covariance matrices. Person-independent experimental results have proved the validity and the good generalization ability of the proposed approach for emotional facial expression recognition. Moreover, compared to the state-of-the-art techniques, the proposed system yields significant improvements in recognizing facial expressions.