A probabilistic model for robust face alignment in videos

A new approach for localizing facial structure in videos is proposed in this paper by modeling shape alignment dynamically. The approach makes use of the spatial-temporal continuity of videos and incorporates it into a statistical shape model which is called constrained Bayesian tangent shape model (C-BTSM). Our model includes a prior 2D shape model learnt from labeled examples, an observation model obtained from observation in the current input image, and a constraint model derived from the prediction by the previous frames. By modeling the prior, observation and constraint in a probabilistic framework, the task of aligning shape in each frame of a video is performed as a procedure of MAP parameter estimation, in which the pose and shape parameters are recovered simultaneously. Experiments on low quality videos from web cameras are provided to demonstrate the robustness and accuracy of our algorithm.