CAJ | 학술논문

为了能够通过保留类内散布矩阵零空间的有效鉴别信息,并选择恰当的投影找到最大可能地区别类内类间的数据集,文章分别选用核主成分分析(KPCA)和零空间线性鉴别分析(null space LDA),其中核主成分分析(KPCA)是主成分分析(PCA)在核空间中的非线性推广,零空间线性鉴别分析利用了零空间的有效信息.文中将KPCA和NS-LDA的特征提取方法结合并应用于人脸识别研究,其综合了KPCA利用数据高阶性和经NS-LDA投影矩阵良好可分性的优点来增强人脸识别性能.实验结果证明,该方法能够有效地提高人脸识别率.
위료능구통과보류류내산포구진령공간적유효감별신식,병선택흡당적투영조도최대가능지구별류내류간적수거집,문장분별선용핵주성분분석(KPCA)화령공간선성감별분석(null space LDA),기중핵주성분분석(KPCA)시주성분분석(PCA)재핵공간중적비선성추엄,령공간선성감별분석이용료령공간적유효신식.문중장KPCA화NS-LDA적특정제취방법결합병응용우인검식별연구,기종합료KPCA이용수거고계성화경NS-LDA투영구진량호가분성적우점래증강인검식별성능.실험결과증명,해방법능구유효지제고인검식별솔.
In order to retain the effective identification information of scattering matrix zero space inside the class,select the appropriate projection to maximize finding the data set inside and between classes,respectively choose Kernel Principal Component Analysis ( KP-CA) and Null Space Linear Discrimination Analysis (NS-LDA),the KPCA is the nonlinear promotion of PCA in the kernel space,NS-LDA takes advantage of zero space information effectively. The feature extraction method of the KPCA and NS-LDA is combined and applied to face recognition research,it combines the advantage of KPCA using data and NS-LDA good separability of projection matrix to enhance face recognition performance. The experimental results show that the method can effectively improve the face recognition rate.