一种改进的局部嵌入网络人脸图像分类方法

Improved face image classification method based on the local embedding network

为提高局部线性嵌入网络在人脸表情识别上的精度以及人脸分类的准确性,提出了一种改进的局部线性嵌入网络人脸图像分类方法。该方法在局部线性嵌入算法的基础上,利用类内-类间判别矩阵作为网络的输入,同时利用重构人脸图像集对局部线性嵌入算法进行改进,并将改进的基于聚类的局部线性嵌入算法嵌入到卷积核的构造过程中,从而增加了不同类别人脸的区分度。通过在Extended Yale B数据集和Olivetti Research Laboratory数据集上进行对比实验,分析了在处理人脸表情和人脸识别任务中不同方法的效果。结果表明,所提出的改进局部线性嵌入网络人脸图像分类方法比文献中其他方法在识别率上提高了11%~26%。

In order to improve the accuracy of facial expression recognition and face classification in a local linear embedding network,an improved face image classification method based on the local linear embedding network is proposed.Based on the local linear embedding algorithm,the intra-class to inter-class discrimination matrix is used as the input of the network.At the same time,the reconstruction of the face image set is used to improve the local linear embedding algorithm,and the improvement of the local linear embedding algorithm based on clustering is embedded into the construction process of the convolution kernel,thus increasing the discrimination degree of different types of faces.By the Extended Yale B data set and Olivetti Research Laboratory data set on the contrast experiment,the experiment is analyzed in the treatment of facial expressions and the effects of various methods in the face recognition task,the results show that,compared with the other methods,the recognition rate of the proposed improved locally linear embedding network face image classification method is raised by 11%~26%.