基于度量优化的保持邻域嵌入的人脸识别  

Face Recognition Based on Metric-optimized Neighborhood Preserving Embedding

作　　者：孙恒义[1] 樊养余[1] 温金环[1] 贾蒙[1] 

机构地区：[1]西北工业大学电子信息学院,西安710129

出　　处：《计算机工程》2011年第4期193-194,197,共3页Computer Engineering

基　　金：国家自然科学基金资助项目(60872159)

摘　　要：监督的保持邻域嵌入算法采用欧氏度量选取k近邻。欧氏度量在数据维数较低时能获得较好的结果,但直接简单地将其从低维空间的应用推广到高维空间中不能取得较好的结果。针对该缺点,提出度量优化的保持邻域嵌入算法。该算法分为无类标号信息(MONPE)和有类标号信息(CLMONPE)2种情况,利用线性判别分析算法降维后的数据选取k近邻。在Yale人脸数据库上的实验结果表明,CLMONPE算法效果较优。Euclidean metric is adopted to look for k-nearest neighbors in the supervised Neighborhood Preserving Embedding（NPE）. However, the results are not very good when Euclidean metric is directly generalized to handle high-dimensional data as dealing with low-dimensional data. To overcome this problem a metric-optimized neighborhood preserving embedding algorithm is proposed in this paper. Two conditions are considered： non-labeled case（MONPE） and labeled case（CLMONPE）. The main idea is to choose k-nearest neighbors by analyzing the data whose dimension is reduced with linear discriminant analysis algorithm. Test result on Yale database shows that CLMONPE has obvious strength in application.

关 键 词：流形学习 人脸识别 监督的保持邻域嵌入 度量优化的保持邻域嵌入 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]