CONDENSATION—Conditional Density Propagation forVisual Tracking
International Journal of Computer Vision
A Probabilistic Exclusion Principle for Tracking Multiple Objects
International Journal of Computer Vision
ICONDENSATION: Unifying Low-Level and High-Level Tracking in a Stochastic Framework
ECCV '98 Proceedings of the 5th European Conference on Computer Vision-Volume I - Volume I
Towards Improved Observation Models for Visual Tracking: Selective Adaptation
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part I
Color-Based Probabilistic Tracking
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part I
Tracking Articulated Body by Dynamic Markov Network
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Spontaneous vs. posed facial behavior: automatic analysis of brow actions
Proceedings of the 8th international conference on Multimodal interfaces
How to distinguish posed from spontaneous smiles using geometric features
Proceedings of the 9th international conference on Multimodal interfaces
Fusion of audio and visual cues for laughter detection
CIVR '08 Proceedings of the 2008 international conference on Content-based image and video retrieval
Emotionally aware automated portrait painting
Proceedings of the 3rd international conference on Digital Interactive Media in Entertainment and Arts
Decision-Level Fusion for Audio-Visual Laughter Detection
MLMI '08 Proceedings of the 5th international workshop on Machine Learning for Multimodal Interaction
Audiovisual laughter detection based on temporal features
ICMI '08 Proceedings of the 10th international conference on Multimodal interfaces
On new design of Kalman filter with entry-wise updating
SMO'08 Proceedings of the 8th conference on Simulation, modelling and optimization
Static vs. dynamic modeling of human nonverbal behavior from multiple cues and modalities
Proceedings of the 2009 international conference on Multimodal interfaces
Tracking by parts: a Bayesian approach with component collaboration
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Applying Affect Recognition in Serious Games: The PlayMancer Project
MIG '09 Proceedings of the 2nd International Workshop on Motion in Games
Is this joke really funny? judging the mirth by audiovisual laughter analysis
ICME'09 Proceedings of the 2009 IEEE international conference on Multimedia and Expo
HCI'07 Proceedings of the 2007 IEEE international conference on Human-computer interaction
Compatible particles for part-based tracking
AMDO'10 Proceedings of the 6th international conference on Articulated motion and deformable objects
Implicit image tagging via facial information
Proceedings of the 2nd international workshop on Social signal processing
Human computing and machine understanding of human behavior: a survey
ICMI'06/IJCAI'07 Proceedings of the ICMI 2006 and IJCAI 2007 international conference on Artifical intelligence for human computing
Trajectory-based representation of human actions
ICMI'06/IJCAI'07 Proceedings of the ICMI 2006 and IJCAI 2007 international conference on Artifical intelligence for human computing
High-performance template tracking
Journal of Visual Communication and Image Representation
Ambient Intelligence in Everyday Life
Output-associative RVM regression for dimensional and continuous emotion prediction
Image and Vision Computing
Comparison of prediction-based fusion and feature-level fusion across different learning models
Proceedings of the 20th ACM international conference on Multimedia
Dynamic probabilistic CCA for analysis of affective behaviour
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part VII
Image and Vision Computing
Bimodal log-linear regression for fusion of audio and visual features
Proceedings of the 21st ACM international conference on Multimedia
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In the recent years particle filtering has been the dominant paradigm for tracking facial and body features, recognizing temporal events and reasoning in uncertainty. A major problem associated with it is that its performance deteriorates drastically when the dimensionality of the state space is high. In this paper, we address this problem when the state space can be partitioned in groups of random variables whose likelihood can be independently evaluated. We introduce a novel proposal density which is the product of the marginal posteriors of the groups of random variables. The proposed method requires only that the interdependencies between the groups of random variables (i.e. the priors) can be evaluated and not that a sample can be drawn from them. We adapt our scheme to the problem of multiple template-based tracking of facial features. We propose a color-based observation model that is invariant to changes in illumination intensity. We experimentally show that our algorithm clearly outperforms multiple independent template tracking schemes and auxiliary particle filtering that utilizes priors.