Supervised sparse manifold regression for head pose estimation in 3D space

In estimating the head pose angles in 3D space by manifold learning, the results currently are not very satisfactory. We need to preserve the local geometry structure effectively and need a learned projective function that can reveal the dominant features better. To address these problems, we propose a Supervised Sparse Manifold Regression (SSMR) method that incorporates both the supervised graph Laplacian regularization and the sparse regression into manifold learning. In SSMR, on the one hand, a low-dimensional projection is embedded to represent intrinsic features by using supervised information while the local structure can be preserved more effectively by using the Laplacian regularization term in the objective function. On the other hand, by casting the problem of learning projective function into a regression with L-1 norm regularizer, a projection is mapped to carry out the sparse representation of high dimension features, rather than a compact linear combination, so as to describe the dominant features better. Experiments show that our proposed method SSMR is beneficial for head pose angle estimation in 3D space. (C) 2014 Elsevier B.V. All rights reserved.