CAJ | 학술논문

提出了一种基于特征融合的人脸识别方法.该方法首先对预处理后的人脸图像进行全局特征及局部分量的提取,分别采用离散余弦交换(DCT)提取包含图像大量信息的低频部分特征和奇异值分解(SVD)抽取图像的代数特征作为图像的全局特征,采用非负矩阵分解(NMF)提取图像的局部分量特征,然后将此两类特征以独立成份分析(ICA)进行融合,获取用于人脸识别的特征向量.在本文的实验中,我们将此特征向量应用于支持向量机(SVM)进行分类训练及识别测试,并获得较好的结果.
제출료일충기우특정융합적인검식별방법.해방법수선대예처리후적인검도상진행전국특정급국부분량적제취,분별채용리산여현교환(DCT)제취포함도상대량신식적저빈부분특정화기이치분해(SVD)추취도상적대수특정작위도상적전국특정,채용비부구진분해(NMF)제취도상적국부분량특정,연후장차량류특정이독립성빈분석(ICA)진행융합,획취용우인검식별적특정향량.재본문적실험중,아문장차특정향량응용우지지향량궤(SVM)진행분류훈련급식별측시,병획득교호적결과.
We proposod a novel algorithm for facial recognition based on features fusion in support vector machine (SVM). First, some local features and global features from pre-processed face images were obtained. The global features were obtained by making use of discrete cosine transform (DCT) and singular value decomposition (SVD). At the same time, the local features by utilizing non-negative matrix factorization (NMF) were also obtained. Furthermore, the feature vectors fused by independent component analysis (ICA)with global and local features were given. Finally, the feature vectors were used to train SVM to realize the face recognition, and the computer simulation illustrated the effectivity of this method on the ORL face database.