基于矩阵体积度量的二维PCA人脸识别(英文)

Two dimensional PCA using matrix volume measure in face recognition

本文提出一种符合高维几何空间理论的矩阵体积度量分类准则用于人脸识别。基于二维PCA的人脸识别方法主要研究的是特征提取部分,对后继的分类识别研究不多。基于二维PCA的人脸识别方法中典型的分类准则是比较特征向量的欧氏距离,而新方法比较的是矩阵的体积。在ORL和AR人脸库上的实验表明,所提出的矩阵体积度量较传统距离度量分类准则更有效。

A novel classification measure based on matrix volume according to the high dimensional geometry theory is proposed for face recognition. Many two dimensional PCA （2DPCA）-based face recognition methods almost pay much attention to the feature extraction, and the classification measure is little investigated. The typical classification measure used in 2DPCA is the sum of the Euclidean distance between two feature vectors in feature matrix, called traditional Distance Measure （DM）. However, this proposed method is to compute the matrix volume. To test its performance, experiments are done based on ORL and AR face databases. The experimental results show the Matrix Volume Measure （MVM） is more efficient than the DM in 2DPCA-based face recognition