Bayesian Tangent Shape Model: Estimating Shape and Pose Parameters via Bayesian Inference
- Yi Zhou ,
- Lie Gu ,
- Hong-Jiang Zhang
Published by Institute of Electrical and Electronics Engineers, Inc.
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.
© 2003 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.