Abstract:

Pose manifold and tensor decomposition are used to represent the nonlinear changes of multi-view faces for pose estimation, which cannot be well handled by principal component analysis or multilinear analysis methods. A pose manifold generation method is introduced to describe the nonlinearity in pose subspace. And a nonlinear kernel based method is used to build a smooth mapping from the low dimensional pose subspace to the high dimensional face image space. Then the tensor decomposition is applied to the nonlinear mapping coefficients to build an accurate multi-pose face model for pose estimation. More importantly, this paper gives a proper distance measurement on the pose manifold space for the nonlinear mapping and pose estimation. Experiments on the identity unseen face images show that the proposed method increases pose estimation rates by 13.8% and 10.9% against principal component analysis and multilinear analysis based methods respectively. Thus, the proposed method can be used to estimate a wide range of head poses.