CAJ | 학술논문

针对人脸识别中的非线性特征提取和有标记样本不足问题，提出了在核空间具有正交性半监督鉴别矢量的计算方法。算法利用核函数将人脸数据映射到高维非线性空间，在该空间采用边界Fisher判别分析（Marginal Fisher Analysis，MFA）算法将少量有类别标签样本进行降维，同时采用无监督鉴别投影（Unsupervised Discriminant Projection， UDP）对大量无标签样本进行学习，以半监督的方法构造算法的目标函数，在特征值求解时以正交方式找出最优投影向量，进行人脸识别。通过实验，在ORL和YALE人脸数据库上验证了该算法的有效性。
침대인검식별중적비선성특정제취화유표기양본불족문제，제출료재핵공간구유정교성반감독감별시량적계산방법。산법이용핵함수장인검수거영사도고유비선성공간，재해공간채용변계Fisher판별분석（Marginal Fisher Analysis，MFA）산법장소량유유별표첨양본진행강유，동시채용무감독감별투영（Unsupervised Discriminant Projection， UDP）대대량무표첨양본진행학습，이반감독적방법구조산법적목표함수，재특정치구해시이정교방식조출최우투영향량，진행인검식별。통과실험，재ORL화YALE인검수거고상험증료해산법적유효성。
In view of the problems of nonlinear feature extraction and use of a few labeled samples in face recognition, a new algorithm of orthogonal optimal semi-supervised discriminant vectors in a kernel space is proposed. Nonlinear kernel mapping is used to map the face data into an implicit feature space. In this space, the MFA can make use of small amount of labeled samples and the UDP can study a large number of unlabeled samples. The object function is defined using the semi-supervised method. Then optimal projection vector is found using orthogonal approach and face recognition is realized. The effectiveness of the proposed methods is validated through the experimental results on ORL and YALE face databases.