摘要
类估计基空间奇异值分解算法(CSVD)克服了奇异值分解(SVD)造成的重构图像基空间不一致的本质缺陷,但在一定程度上削弱了图像的类别特征。二维非负矩阵分解算法(2DNMF)能在一定程度上避免NMF识别算法中因图像向量化而造成的结构信息丢失、内存花销大等不足,但是随着训练样本数量的增多,迭代速度慢、训练时间长等缺陷也将凸显。根据CSVD与2DNMF的优缺点,提出了人脸识别的联合CSVD-2DNMF算法,进而运用提出的算法在Matlab平台上对ORL人脸数据库中的人脸图像进行了识别实验。实验结果表明该算法能有效的缩短训练时间和提高识别率。
Although CSVD can eliminate the SVD-caused intrinsic defect that the basic spaces of reconstructed image are disagreed, the features of image classification are not impaired. Because of the image vectorization, the face recognition algorithm based on NMF will cause the lost of structure information and takes more memory. Al- though 2DNMF avoid these shortcomings caused by NMF, its own defect that the slow iterative convergence speed and the long training time will appear along with the increase of training samples. By combining the advantages and disadvantages of CSVD and 2DNMF, the joint CSVD-2DNMF face recognition algorithm is advanced. Experimental results from ORL face image database by using Matlab show that the efficiency of this advanced fusion method can shorten training time and improve recognition rates effectively.
出处
《科学技术与工程》
北大核心
2012年第29期7616-7620,共5页
Science Technology and Engineering
基金
航空基金(20112096016)资助
