基于类内均值的双投影2DPCA人脸识别方法
贝宗钧;朱煜
【期刊名称】《计算机仿真》 【年(卷),期】2009(026)010
【摘要】It is a key problem to obtain robust and appropriately low - dimensioned face features in the face recognition task. In this paper, a method based on two directional and two dimensional principal component analysis and combined with the within -class average value is presented for face recognition. First, the training samples are normalized by using the corresponding within - class average value, in which the classification distance of between -class samples is enlarged, while that of the within - class is reduced. Then the image covariance matrix is calculated to obtain a family of the optimal feature vectors. All the processes are performed in both horizontal and vertical directions sequentially to compress the coefficients of the feature vectors. The experimental results on ORL and Yale data-bases show that the method proposed in this paper has better robustness to the influences of greatly illuminative and expressive changes. At the same time, it also can achieve a higher recognition rate than the traditional 2DPCA and is of some practical significance.%在人脸识别问题中,如何提取具有鲁棒性的人脸特征和降低特征维数是两个关键.根据二维主成分分析方法直接利用二维图像来构建方差矩阵的优点,引入了类内均值的思想,首先计算每类训练样本的类内平均
基于类内均值的双投影2DPCA人脸识别方法
