基于有监督的Laplacianfaces和局部二元模式的人脸表情识别

由于不能准确反映表情流行的内在结构等缺点,目前基于流行的表情识别方法的识别不高.针对此问题提出了基于有监督的Laplacianfaces(SLAP)和局部二元模式(LBP)的方法,简称LSLAP.SLAP的优点是保留局部结构的同时把类别信息加入到映射中,因此它得到的子空间中不同类别的样本可以被很好地区别开来;LBP在3种级别的区域上描述人脸具有判别力强、抗光线干扰和计算简便等优点.通过把人脸的LBP特征映射到由SLAP得到的低维空间以达到最终的特征提取,采用最近邻分类器进行分类.在JAFFE和Cohn Kanade人脸表情库上对该算法进行验证,实验结果表明:SLAP能够准确地反映表情流形的内在结构,与EigenfacesFisherfaces、Laplacianfaces以及它们各自应用于LBP的方法即Eigenfaces LBP,Fisherfaces LBP,Laplacianfaces LBP相比,LSLAP具有更高的识别率.

Local binary patterns based facial expression recognition using supervised laplacianfaces

Nowadays,many approaches based on manifold can't reflect accurately the underlying structure of expressional manifold,so their recognition accuracy is not high.Aimed at this problem,this paper proposed a method based on supervised Laplacianfaces(SLAP)and local binary patterns(LBP).LSLAP is short for the proposed method.The advantages of SLAP are preserving locality and considering the class information into account,which make the different sample classes well separated in the subspace of SLAP.LBP have the a...