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.