Combining manifold learning and nonlinear regression for head pose estimation
流形学习与非线性回归结合的头部姿态估计

Manifold learning attempts can be used to obtain the intrinsic structure of the non-linear data, which can be used in non-linea dimensionality reduction. The general regression neural network (GRNN) is a kind of artificial neural network, which can be used in non-linear regression. In this paper, the ManiNLR method, which is based on manifold learning and nonlinear regression, is proposed for head pose estimation. ManiNLR performs manifold learning on the digital image,and then uses GRNN to map the data into the linear separable space,finally using the result to estimate the head pose. Experiments show that ManiNLR can better estimate the head pose in digital images,and has the advantages of high speed and high robustness.