We present a generic framework to track shapes across large variations by learning non-linear shape manifold as overlapping, piece-wise 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.

We present algorithms for recognizing human motions and facial expressions in monocular video sequences, based on discriminative Conditional Random Field (CRF). Existing approaches make simplifying, often unrealistic assumptions on the conditional independence of observations given the motion class labels and cannot accommodate overlapping features or long term contextual dependencies in the observation sequence. In contrast, conditional models like the CRFs seamlessly represent contextual dependencies, support efficient, exact inference using dynamic programming, and their parameters can be trained using convex optimization.