CAJ | 학술논문

人脸识别具有小样本、高维等特性。典型相关分析算法（CCA）无法准确提取人脸识别特征，不能准确刻画人脸图像的局部变化，导致人脸识别率低。为提高人脸识别率，提出一种核主成分分析与典型相关分析相融合的人脸识别算法（KPCA-CCA）。首先将人脸图像划分多个子模块，然后提取局部特征，同时采用KPCA提取全局特征，并采用CCA将两种特征进行融合，降低特征向量的维数，最后采用子模式进行人脸识别，以投票方式确定人脸的类别。采用AR与Yale数据集对KPCA-CC性能进行测试，仿真结果表明，相对于对比模型，KPCA-CCA提高了人脸识别的识别率。
인검식별구유소양본、고유등특성。전형상관분석산법（CCA）무법준학제취인검식별특정，불능준학각화인검도상적국부변화，도치인검식별솔저。위제고인검식별솔，제출일충핵주성분분석여전형상관분석상융합적인검식별산법（KPCA-CCA）。수선장인검도상화분다개자모괴，연후제취국부특정，동시채용KPCA제취전국특정，병채용CCA장량충특정진행융합，강저특정향량적유수，최후채용자모식진행인검식별，이투표방식학정인검적유별。채용AR여Yale수거집대KPCA-CC성능진행측시，방진결과표명，상대우대비모형，KPCA-CCA제고료인검식별적식별솔。
Face recognition has the features of small sample and high-dimensionality.Canonical correlation analysis (CCA)can’t accu-rately extract the features of face recognition,nor accurately depicts the local variations of face image as well,which lead to low face recogni-tion rate.In order to improve the face recognition rate,in this paper we propose a novel face recognition algorithm which fuses kernel princi-pal component analysis and canonical correlation analysis (KPCA-CCA).First,it divides the face image into multiple sub-models and ex-tracts local features,meanwhile the KPCA is employed to extract global features,and then these two kinds of features are fused by CCA to re-duce the dimensionality of eigenvectors,finally the sub-models are used for face recognition,and the face type is determined by voting.The performance of KPCA-CCA has been tested by AR and Yale datasets,simulation results show that it raises face recognition rate with respect to the reference model.