%0 Journal Article
%T Combining manifold learning and nonlinear regression for head pose estimation<br>流形学习与非线性回归结合的头部姿态估计
%A Fan Jinfu
%A Chen Duansheng
%A <br>范进富
%A 陈锻生
%J 中国图象图形学报
%D 2012
%I 
%X 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.
%K manifold learning
%K head pose estimation
%K nonlinear regression
%K artificial neural network<br>流形学习
%K 头部姿态估计
%K 非线性回归
%K 人工神经网络
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=5B3AB970F71A803DEACDC0559115BFCF0A068CD97DD29835&cid=8240383F08CE46C8B05036380D75B607&jid=D06194629680C940ACE75262F54B9D85&aid=0EB9A27D923E916606AC9D1F1D3F2497&yid=99E9153A83D4CB11&vid=BCA2697F357F2001&iid=5D311CA918CA9A03&sid=15A3E3A739C4EF3F&eid=BF1CF7F9466F9169&journal_id=1006-8961&journal_name=中国图象图形学报&referenced_num=0&reference_num=14