Pose Estimation Based on Gaussian Error Models

In this paper, a new method is presented to estimate the 3D pose of facial image based on statistical Gaussian error models. The basic idea is that the pose angle can be computed by the orthogonal projection computation if the specific 3D shape vector of the given person is known. In our algorithm, Gaussian probability density function is used to model the distributions of the 3D shape vector as well as the errors between the orthogonal projection computation and the weak perspective projection. By using the prior knowledge of the errors distribution, the most likely 3D shape vector can be referred by the labeled 2D landmarks in the given facial image according to the maximum posterior probability theory. Refining the error term, thus the pose parameters can be estimated by the transformed orthogonal projection formula. Experimental results on real images are presented to give the objective evaluation.