Bayesian tangent shape model: estimating shape and pose parameters via Bayesian inference

In this paper we study the problem of shape analysis and its application in locating facial feature points on frontal faces. We propose a Bayesian inference solution based on tangent shape approximation called Bayesian tangent shape model (BTSM). Similarity transform coefficients and the shape parameters in BTSM are determined through MAP estimation. Tangent shape vector is treated as the hidden state of the model, and accordingly, an EM based searching algorithm is proposed to implement the MAP procedure. The major results of our algorithm are: 1) tangent shape is updated by a weighted average of two shape vectors, the projection of the observed shape onto tangent space, and the reconstruction of shape parameters. 2) Shape parameters are regularized by multiplying a ratio of the noise variations, which is a continuous function instead of a truncated function. We discussed the advantages conveyed by these results, and demonstrate the accuracy and the stability of the algorithm by extensive experiments.