Robust Parameterized Component Analysis
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part IV
Probabilistic Human Recognition from Video
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part III
Robust parameterized component analysis: theory and applications to 2D facial appearance models
Computer Vision and Image Understanding - Special issue on Face recognition
Spatio-temporal graphical-model-based multiple facial feature tracking
EURASIP Journal on Applied Signal Processing
Similarity Features for Facial Event Analysis
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part I
Incremental perspective motion model for rigid and non-rigid motion separation
PSIVT'07 Proceedings of the 2nd Pacific Rim conference on Advances in image and video technology
Conic-based algorithm for visual line estimation from one image
FGR' 04 Proceedings of the Sixth IEEE international conference on Automatic face and gesture recognition
FGR' 04 Proceedings of the Sixth IEEE international conference on Automatic face and gesture recognition
Estimating the visual direction with two-circle algorithm
SINOBIOMETRICS'04 Proceedings of the 5th Chinese conference on Advances in Biometric Person Authentication
A bayesian estimation approach to super-resolution reconstruction for face images
IWICPAS'06 Proceedings of the 2006 Advances in Machine Vision, Image Processing, and Pattern Analysis international conference on Intelligent Computing in Pattern Analysis/Synthesis
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This paper describes an unified probabilistic framework for appearance based tracking of rigid and non-rigid objects. A spatio-temporal dependent shape/texture Eigenspace and mixture of diagonal gaussians are learned in a Hidden Markov Model(HMM) like structure to better constrain the model and for recognition purposes. Particle filtering is used to track the object while switching between different shape/texture models. This framework allows recognition and temporal segmentation of activities. Additionally an automatic stochastic initialization is proposed, the number of states in the HMM are selected based on the Akaike Information Criterion and comparison with deterministic tracking for 2D models is discussed. Preliminary results of eye-tracking, lip-tracking and temporal segmentation of mouth events are presented.