一种人脸图像特征提取的局部和整体间距嵌入方法

针对边界Fisher分析(MFA)构建的惩罚图没有充分描述类间数据分离度的缺点,提出一种局部和整体间距嵌入(LGME)特征提取方法。该方法在构建惩罚图时采用了全部的不同类样本数据对,并适当地强调了间距较小的不同类样本数据对的作用。与MFA相比,LGME同时使用类间数据的局部和整体间距信息,对类间数据分离度进行了充分描述,从而使其提取的数据特征具有更强的判别力。实验结果表明,LGME方法提取的人脸图像特征在用于人脸识别时,具有较高的识别率,且更具鲁棒性。

Local and Global Margin Embedding Method for Feature Extraction of Face Image

To overcome the disadvantage that the penalty graph constructed by marginal Fisher analysis (MFA) can't sufficiently describe interclass separabihty, this paper proposed a novel feature extraction method, called local and global margin embedding (LGME). In LGME, all interclass data pairs are used to construct penalty graph, whereas the importance of limited interclass data pairs with minimal margins is emphasized properly. Compared with MFA, I_GME simultaneity uses local and global interclass margin to characterize interclass separability, so the data features extracted by LGME have more discriminative power. hhe experimental results show that the face image features extracted by LGME for face recognition have higher recognition rate and more robust.