FLDA方法在单样本人脸识别中的应用研究  

Research of Face Recognition with FLDA from Single Sample per Person

作　　者：倪剑虹[1] 王晶[1] 崔玉红[1] 刘永锋[2] 

机构地区：[1]河北体育学院现代教育技术中心,河北石家庄050041 [2]石家庄陆军指挥学院信息管理中心,河北石家庄050084

出　　处：《电视技术》2013年第15期181-184,共4页Video Engineering

摘　　要：在人脸识别应用中,当每个人有多个训练样本(MSPP)时,Fisher线性判别分析(FLDA)方法可以很好地用于特征提取。然而,当每个人只有一个训练样本(SSPP)时,因为类内散布矩阵为零矩阵,所以FLDA方法将不能使用。为了解决该问题,提出了一种比较新颖的方法来估计类内散布矩阵,借助于奇异值分解(SVD)方法,先将人脸图像分解成两部分,然后分别估计出类内散布矩阵及类间散布矩阵,使FLDA方法能够得到有效的应用。在ORL及Yale上的实验表明了提出的方法比现有的许多方法取得了更好的识别效果。Usually, Fishier Linear Discriminative Analysis （FLDA） can be effective in face recognition when each person has multiple samples （MMSP）. However,it will not be used when each person has only one training sample （SSPP） because the intra-class metric is zero. To address this problem ,a novel method to estimate the intra-class scatter metric is proposed. By using the Singular Value Decomposition （SVD） ,firstly, face image is decomposed into two parts,and then they are used to estimate intra-class and inter-class scatter metrics,which making the traditional FLDA can be applied to SSPP task. Experiments on the ORL and Yale face database show that the proposed method can achieve better recognition accuracy than many common solutions to the SSPP problem.

关 键 词：单训练样本每人 奇异值分解 FISHER线性判别分析 

分 类 号：TN911.73[电子电信—通信与信息系统]