基于表情加权距离SLLE的人脸表情识别

局部线性嵌入(LLE)算法没有考虑训练样本的类别信息，而有监督LLE(SLLE)算法等同处理类别之间的差异性。根据人脸表情的特点，各个表情类别之间的差异性是有区别的，据此，文中构造一种基于表情加权距离的SLLE算法。在计算训练样本之间距离时，对来自不同表情类别的样本距离选择不同的加权值，从而使表情类别的先验信息得到更充分利用。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，该算法在一定邻域范围内获得更好的人脸表情识别率，是一种有效算法。

Facial Expression Recognition Based on SLLE with Expression Weighted Distances

The traditional locally linear embedding (LLE) algorithm doesn’t take into consideration the class label information of training samples while the supervised LLE (SLLE) algorithm treats the different between classes equally which is inappropriate for facial expression recognition. Taking the differences between expressions into account, a new supervised locally linear embedding (SLLE) algorithm called expression related SLLE (ERSLLE) is designed which uses different weights for sample distance calculation in determining neighborhood samples. The proposed algorithm is applied to the facial expression recognition on the Japanese female facial expression database (JAFFE). The results show that the proposed algorithm is effective and superior to the traditional LLE and SLLE. Better performance is obtained for facial expression recognition in a certain range of the number k of the nearest neighborhood, compared with LLE and SLLE.