计算机工程与科学2017，Vol.39Issue(4)：777-784,8.DOI:10.3969/j.issn.1007-130X.2017.04.023
一种基于块共同特征值的人脸识别方法
A face recognition method based on block common eigenvalues
摘要
Abstract
The principal component analysis and linear discriminant analysis are important face recognition methods.Both of them can realize feature extraction by solving the eigenvalue problem.However,the small sample and singularity problem can be caused by the curse of dimensionality.We propose a simple face recognition method,which can effectively reduce computation cost without singular value decomposition.Firstly,we divide the image into blocks,and then calculate the polynomial coefficients to obtain the companion matrix which is used for feature extraction.A symmetric matrix based on the polynomial companion matrix of two different images is calculated.Finally,the nullity of symmetric matrix is calculated to recognize similar face images.Experiments on the ORL,Yale and FERET face databases verify the effectiveness of the proposed method.The results show that this method has high recognition performance in recognizing the face with big variation in pose and illumination.关键词
特征提取/人脸识别/友阵/分块图像Key words
feature extraction/face recognition/companion matrix/block image分类
信息技术与安全科学引用本文复制引用
崔鹏,张雪婷..一种基于块共同特征值的人脸识别方法[J].计算机工程与科学,2017,39(4):777-784,8.基金项目
国家自然科学基金(61370086) （61370086）
黑龙江省自然科学基金(F2015038) （F2015038）
