基于Gabor变换与改进SLLE的人脸表情识别

本文通过Gabor变换进行人脸表情图像的特征提取，并利用局部线性嵌入（LLE）系列算法进行数据降维操作.LLE算法是一种非线性降维算法，它可以使得降维后的数据保持原有的拓扑结构，在人脸表情识别中有广泛的应用.因为LLE算法没有考虑样本的类别信息，因此有了监督的局部线性嵌入（SLLE）算法.但是SLLE算法仅仅考虑了样本的类别信息却没有考虑到各种表情之间的关系，因此本文提出一种改进的SLLE算法，该算法认为中性表情是其他各种表情的中心.在JAFFE库上进行人脸表情识别实验结果表明，相比LLE算法和SLLE算法，该算法获得了更好的人脸表情识别率，是一种有效算法.

Facial Expression Recognition Based on Gabor Transform and Improved SLLE

In this paper, we adopt the Gabor transform to extract features of facial expression images and use a series of locally linear embedding(LLE) algorithms to reduce the data dimension. The LLE algorithm widely used in facial expression recognition is a kind of nonlinear dimension reduction algorithm. It is able to make dimension-reduced data keep the original topology. Because the LLE algorithm does not take the category information of samples into account, the supervised locally linear embedding(SLLE) algorithm appears. But the SLLE algorithm only considers the category information of samples, and does not take the relationship among various expressions into account. Therefore, in this paper, we propose an improved SLLE algorithm, which regards the neutral expression as the center of the other expressions. The results of facial expression recognition experiments on the JAFFE database show that our algorithm obtained better facial expression recognition rate compared with the LLE algorithm and the SLLE algorithm. Our algorithm is more effective