Face alignment based on fusion subspace and 3D fitting

Abstract

Abstract The traditional face alignment approaches based on cascade regression have achieved satisfactory result on the frontal face, but for the face with large changes in posture and expression, a single initial shape will lead to the result falling into local optimum. In order to solve this problem, a two‐stage cascade regression model for face alignment is proposed, which generates coarse initial shape from the aligned salient shape. The first stage is used to align the salient shape that contains some prominent landmarks. To enhance the robustness of authors' method, the fusion subspace is used to divide the samples, and each subset trains cascade regression model separately. The alignment results of the first stage are used to generate the coarse initial shapes for the second stage through 3D fitting. The second stage is still based on cascade regression, which is used to further predict the full shape. The experimental results demonstrate the proposed method can achieve state‐of‐art performance, especially in unconstrained conditions with various poses.