基于特征层融合的人脸识别新方法  

Face recognition based on feature level fusion

作　　者：刘敏[1] 李晓东[2] 王振海[1] 

机构地区：[1]临沂师范学院信息学院,山东临沂276005 [2]东南大学复杂工程系统测量与控制教育部重点实验室,南京210096

出　　处：《计算机应用》2009年第10期2662-2664,共3页journal of Computer Applications

摘　　要：为了探索人脸识别中有效的特征提取方法,提出了一种基于特征层融合的算法。该方法融合了保局投影(LPP)和最大间距准则(MMC)两种方法。首先对训练样本进行LPP判别分析,得到每个训练样本在LPP子空间上的投影,然后利用MMC方法对所有的投影进行鉴别分析,提取出更有效的样本判别特征;采用最小近邻分类器分类。在ORL人脸库的测试结果表明,在姿态、光照、表情、训练样本数目变化的情况下,该算法都具有较好的识别率。In order to search for an efficient feature extraction approach for face recognition, a method based on feature level fusion was presented. This method integrated Locality Preserving Projection （LPP） with Maximum Margin Criterion （ MMC）. Firstly, LPP was performed on training samples set, so the projection of each training sample on LPP subspaee could be got. Further, MMC algorithm was performed on all the obtained projections to get more efficient discriminant features for recognition. Nearest Neighborhood （NN） algorithm was used to construct classifiers. The experiments on the ORL face database show that the recognition rate of the proposed method is high when pose, illumination condition, face expression and training sample number change.

关 键 词：人脸识别 保局投影 最大间距准则 最大散度差 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]