摘要
流形学习的目的是发现非线性数据的内在结构,可用于非线性降维。广义回归网络是人工神经网络的一种,可用于非线性回归。基于流形学习和非线性回归,提出了用于解决头部姿态估计的ManiNLR方法。该方法首先用流形学习对图像数据进行降维,然后用非线性回归的方法将数据映射到线性可分空间,利用非线性回归的结果对人脸的头部姿态进行估计。实验结果表明,ManiNLR算法能够较好地估计图像中的头部姿态,并具有较快的速度和较高的鲁棒性。
Manifold learning attempts can be used to obtain the intrinsic structure of the non-linear data, which can be used in nondinea 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.
出处
《中国图象图形学报》
CSCD
北大核心
2012年第8期1002-1010,共9页
Journal of Image and Graphics
基金
国家自然科学基金项目(10901062)
福建省自然科学基金项目(2010J01337)
福建省高校产学合作科技重大项目(2011H6010)
福建省科技计划重点项目(2011H0028)
福建省仿脑智能系统重点实验室开放课题(BLISSOS2010101)
关键词
流形学习
头部姿态估计
非线性回归
人工神经网络
manifold learning
head pose estimation
nonlinear regression
artificial neural network
