基于模糊决策和MSD的单样本人脸识别算法  

Face recognition with single training sample based on fuzzy decision and MSD

作　　者：王晓洁[1] 李晓东[1] 

机构地区：[1]临沂大学信息学院,山东临沂276005

出　　处：《计算机工程与应用》2011年第19期200-202,共3页Computer Engineering and Applications

摘　　要：当每个人只有一个训练样本时,最大散度差鉴别分析在人脸识别中的识别性能会降低,为了解决这一问题,提出了基于模糊决策和最大散度差鉴别分析的单样本人脸识别算法。通过对每个训练样本进行适当的分块,从而获得较多的训练样本个数,在这些新的训练样本集上应用类内中间值最大散度差鉴别分析算法得到最优投影矩阵,并基于这个最优投影矩阵可以计算训练样本和待测试样本的特征。对模糊决策方法进行分类。在著名的ORL和FERET人脸数据库上的大量实验结果表明,该算法可以提高识别率。To improve the recognition performance of face recognition with single training sample,a face recognition method based on fuzzy decision and maximum scatter difference with single training sample is proposed in this paper.In this method,each training sample is portioned several blocks to increase the number of the element of the training sample set,on which the maximum scatter difference algorithm is performed to get the optimal projection matrix.Therefore,the features of the training sample and testing facial images can be obtained by projecting them on the optima projection matrix achieved above.During the recognition stage,the fuzzy decision is used to do the classification.Extensive experiment results on ORL and FERET illustrate the feasibility of the proposed method.

关 键 词：人脸识别 最大散度差鉴别分析 模糊决策 单训练样本 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]