模块2DPCA的缺陷与改进

朱明旱; 罗大庸

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DOI: 10.11834/jig.20090117
2009 | Volume 14 | Number 1

模块2DPCA的缺陷与改进

朱明旱^1,2, 罗大庸¹(1.中南大学信息科学与工程学院,长沙 410083;2.湖南文理学院电气与信息工程学院，常德 415000)

摘要

模块2DPCA是2DPCA的推广，在识别性能上比2DPCA更具鲁棒性。本文分析了模块2DPCA在计算训练样本总体散布矩阵和本征向量选取方面的缺陷，提出了一种改进的模块2DPCA算法。实验结果表明，改进后的算法能更好地选取本征向量，更有效地提取人脸特征。

关键词

模块2DPCA 本征向量特征提取人脸识别

Defects and Improvement of Modular Two-dimensional Principal Component Analysis

ZHU Minghan^1,2, LUO Dayong¹(1.College of Information Science and Engineering, Central South University, Changsha 410083;2.College of Communication and Electric Engineering, Hunan University of Arts and Science,Changde 415000)

Abstract

Modular 2DPCA is an extension of 2DPCA algorithm. The recognition performance of modular 2DPCA is more robust than that of 2DPCA.In this paper, the defects of modular 2DPCA about computing the total scatter matrix of training samples and selecting eigenvectors are analyzed. An improved modular 2DPCA algorithm is presented. Experiments show that the improved modular 2DPCA algorithm can select better eigenvectors and extract facial features more effectively.

Keywords

modular two-dimensional principal component analysis eigenvector feature extraction face recognition

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