首页> 外文会议>Pattern Recognition, 2009. CCPR 2009 >Two-Dimensional Local Graph Embedding Discriminant Analysis(F2DLGEDA) with Its Application to Face and Palm Biometrics
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Two-Dimensional Local Graph Embedding Discriminant Analysis(F2DLGEDA) with Its Application to Face and Palm Biometrics

机译:二维局部图嵌入判别分析(F2DLGEDA)及其在人脸和手掌生物识别中的应用

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In two-dimensional local graph embedding discriminant analysis(2DLGEDA), the intrinsic graph characterizes the intraclass compactness and connects each data point with its neighboring within the same class, while the penalty graph connects the marginal points and characterizes the interclass separability. But in the real world, face images are always affected by variations in illumination conditions and different facial expressions. So, the fuzzy two-dimensional local graph embedding analysis (F2DLGEA) algorithm is proposed, in which the fuzzy k-nearest neighbor (FKNN) is implemented to achieve the distribution local information of original samples. Experimental results on ORL face databases and PolyU palmprint show the effectiveness of the proposed method.
机译:在二维局部图嵌入判别分析(2DLGEDA)中,内在图表征类内紧度并将每个数据点与其同一类内的相邻数据点连接,而惩罚图则连接边缘点并表征类间可分离性。但是在现实世界中,面部图像始终受照明条件变化和面部表情不同的影响。因此,提出了一种模糊二维局部图嵌入分析(F2DLGEA)算法,实现了模糊k最近邻(FKNN)算法来实现原始样本的分布局部信息。在ORL人脸数据库和PolyU手掌上的实验结果证明了该方法的有效性。

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