基于核主元分析的头部姿势估计  

Head Pose Estimation Based on Kernel Principal Component Analysis

作　　者：路玉峰[1] 王增才[1] 李学勇[1] 

机构地区：[1]山东大学机械工程学院,济南250061

出　　处：《光电工程》2008年第8期62-65,77,共5页Opto-Electronic Engineering

基　　金：教育部博士点基金资助项目(20060422011)

摘　　要：现有头部姿势估计方法主要是基于几何分析和基于外观线性变换的方法,计算复杂、通用性不强。提出一种新的利用非线性的核变换算法进行姿势估计的方法,根据流形学习理论,不同姿势的高维人脸图像存在一低维流形结构,提取该流形结构可估计头部姿势。核主元分析是一种非线性降维算法,能够把这种流形结构嵌入到低维空间。利用核主元分析训练姿势估计曲线,然后把新图像投影到姿势曲线上,利用插值方法估计新投影点对应得姿势角度。核主元分析的方法克服了传统线性估计方法的缺点,实验证明该方法估计效果良好,并给出进一步提高估计效果的途径。The present pose estimation methods are mainly based on geometry analysis or linear transformation, which are complex and are not universal. A new method is proposed based on nonlinear transformation. According to manifold learning theory, different head poses lie on some low dimensional manifolds. Kernel Principal Component Analysis （KPCA） is a nonlinear dimension reduction method. The hidden manifold in the high dimensional space can be successfully embedded to a low dimensional space using KPCA. A pose curve is gotten using KPCA train samples and new pose image is projected onto this curve. The pose angle can be estimated using interpolation method. The disadvantage of traditional linear method is conquered by KPCA and the experimental result shows that the method is effective to estimate head poses. The method to improve the estimating result is suggested based on the experiments.

关 键 词：头部姿势估计 核主元分析 流形学习 非线性降维 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]