自适应半监督边界费舍尔分析

Adaptive Semi-supervised Marginal Fisher Analysis

基于图的半监督算法已经成功地应用于人脸识别中,算法不仅考虑带标签数据而且利用一致性的假设。传统的算法一致性约束是定义在原特征空间中,但是在原特征空间中定义的一致性不是最好的。提出了自适应半监督边界费舍尔分析算法,它将一致性约束定义在原特征空间和期望低维特征空间中。在CMU PIE和YALE-B数据库上进行了实验,结果表明自适应半监督边界费舍尔分析算法在人脸识别率上有显著的提高。

Graph based semi-supervised methods have successfully used in face recognition.These algorithms not only consider the label information,but also utilize a consistency assumption.Conventional algorithms assumed that the consistency constraint is defined on the original feature space.However,the original feature space is not the best for defining consistency.We proposed adaptive semi-supervised marginal fisher analysis（ASMFA） by which the consistency constraint is defined in the original feature space and the expected low-dimensional feature space.Experimental results on the CMU PIE and YALE-B databases demonstrate that ASMFA brings signification improvement in face recognition accuracy.