吉首大学学报：自然科学版
吉首大學學報：自然科學版
길수대학학보：자연과학판
Journal of Jishou University(Natural Science Edition)
2011年
3期
55-58
,共4页
线性变换%人脸识别%PCA%2DPCA%PCA+2DPCA
線性變換%人臉識彆%PCA%2DPCA%PCA+2DPCA
선성변환%인검식별%PCA%2DPCA%PCA+2DPCA
linear transformation%face recognition%PCA%2DPCA%PCA＋2DPCA
阐述了基于主成分分析（Principal Component Analysis,PCA）和二维主成分分析（2DPCA）的人脸识别方法,分析了该方法在矩阵理论中的来源和算法,提出了PCA＋2DPCA分析方法,并采用2DPCA求出特征向量,PCA进行最优压缩,从而降低了维数.
闡述瞭基于主成分分析（Principal Component Analysis,PCA）和二維主成分分析（2DPCA）的人臉識彆方法,分析瞭該方法在矩陣理論中的來源和算法,提齣瞭PCA＋2DPCA分析方法,併採用2DPCA求齣特徵嚮量,PCA進行最優壓縮,從而降低瞭維數.
천술료기우주성분분석（Principal Component Analysis,PCA）화이유주성분분석（2DPCA）적인검식별방법,분석료해방법재구진이론중적래원화산법,제출료PCA＋2DPCA분석방법,병채용2DPCA구출특정향량,PCA진행최우압축,종이강저료유수.
This paper mainly introduces the application of linear transformation matrix in pattern recognition,face recognition based on Principal Component Analysis（PCA） and Two-dimensional Principal Component Analysis（2DPCA）.and the source and algorithm in matrix theory.A kind of innovative method of analyzing is put forward,namely PCA＋2DPCA,which is to get the engenvector through 2DPCA and achieve the optimal compress through PCA,and thus reduce the number of dimensions.
