Visual learning and recognition of 3-D objects from appearance
International Journal of Computer Vision
Non-linear point distribution modelling using a multi-layer perceptron
BMVC '95 Proceedings of the 1995 British conference on Machine vision (Vol. 1)
A Bayesian Mixture Model for Multi-View Face Alignment
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Spatial and temporal pyramids for grammatical expression recognition of American sign language
Proceedings of the 11th international ACM SIGACCESS conference on Computers and accessibility
Motion profiles for deception detection using visual cues
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part VI
Facial expressions in American sign language: Tracking and recognition
Pattern Recognition
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We present a generic framework to track shapes across large variations by learning non-linear shape manifold as overlapping, piecewise linear subspaces. We use landmark based shape analysis to train a Gaussian mixture model over the aligned shapes and learn a Point Distribution Model(PDM) for each of the mixture components. The target shape is searched by first maximizing the mixture probability density for the local feature intensity profiles along the normal followed by constraining the global shape using the most probable PDM cluster. The feature shapes are robustly tracked across multiple frames by dynamically switching between the PDMs. Our contribution is to apply ASM to the task of tracking shapes involving wide aspect changes and generic movements. This is achieved by incorporating shape priors that are learned over non-linear shape space and using them to learn the plausible shape space. We demonstrate the results on tracking facial features and provide several empirical results to validate our approach. Our framework runs close to real time at 25 frames per second and can be extended to predict pose angles using Mixture of Experts.