Supervised locality discriminant manifold learning for head pose estimation

作者：

Highlights：

• We propose a novel supervised locality discriminant manifold learning approach.

• We combine the discriminant graph embedding and Laplacian regularized least square.

• We design an optimal supervised weight for estimating head pose more accurately.

摘要

•We propose a novel supervised locality discriminant manifold learning approach.•We combine the discriminant graph embedding and Laplacian regularized least square.•We design an optimal supervised weight for estimating head pose more accurately.

论文关键词：Manifold learning,Supervised learning,Locality discriminant regularization,Head pose estimation,Graph construction

论文评审过程：Received 20 March 2013, Revised 6 April 2014, Accepted 10 April 2014, Available online 6 May 2014.

论文官网地址：https://doi.org/10.1016/j.knosys.2014.04.028