基于位平面图像与2DMSLDA的单样本人脸识别  

Face recognition with single training sample per person based on bit-planes image and 2DMSLDA

作　　者：刘永俊[1,2] 常晋义[1] 陈才扣[2,3] 杨静宇[3] 

机构地区：[1]常熟理工学院软件工程系,江苏常熟215500 [2]扬州大学信息工程学院,江苏扬州225009 [3]南京理工大学计算机科学与工程系,南京210094

出　　处：《计算机工程与应用》2010年第15期172-175,共4页Computer Engineering and Applications

基　　金：国家自然科学基金No.60472060;江苏省博士后科研资助计划项目;江苏省高校自然科学基金(No.05KJB520152)~~

摘　　要：在进行单训练样本人脸识别时,基于每人多个训练样本的传统人脸识别算法效果通常不太理想。尤其是基于Fisher线性鉴别准则的一些方法,由于类内散布矩阵为零矩阵,根本无法进行识别。针对以上问题进行了分析研究,提出了一种新的样本扩充方法,即:采用位平面图像分解法,将每幅样本图像分解为8幅,进而通过各种合成策略构造多幅样本图像。使用一种更加稳定的二维最大散度差线性鉴别分析方法(2DMSLDA)对上面获得的新样本图像进行特征抽取。在ORL国际标准人脸库上进行的实验表明了所提算法的可行性和有效性。For face recognition with single training sample per person,the conventional face recognition methods which work with many training samples don’t function well.Especially,a number of methods based on Fisher linear discriminant criterion can’t work because the within-class scatter matrix is a matrix with all elements being zero.To overcome the above problem,a new sample augment method is proposed in this paper.It slices the image at eight different planes（bit-planes）.It augments new training samples by combining the bit-planes images.Two-dimensional maximum scatter-difference discriminant analysis is performed on the new training images obtained.The experimental results on ORL face database show that the proposed method is effective and promising in face recognition with single training sample per person.

关 键 词：人脸识别 位平面图像 二维最大散度差线性鉴别分析 单样本 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]