本文研究了基于Isomap的非线性降维方法，对由面部表情序列提取的面部动画参数特征进行降维，
分析了降维后的流形特征空间与认知心理学情感空间之间的关系。实验结果表明，Isomap降维后的情感流
形特征能够表现情感的强度变化，而且比PCA降维特征对情感强度的描述更加合理和平滑；情感识别实验
也表明，使用Isomap降维流形特征的识别率要高于原始情感特征和PCA降维特征，而且对各种情感的识别
结果更加均衡。

This paper studies the Isomapbased nonlinear dimensionality reduction and
visualization method,applies it to the feature reduction of facial animation parameters of
emotional facial image sequences,and analyzes the relationship between the reduced
dimension space and the cognitive psychological emotion space.The experimental results show
that the coordinate of the manifold feature in the reduceddimension space shows the
emotion intensity of the corresponding image frame,and the intensity curve of an emotional
facial sequence from Isomap is more reasonable and smooth than that from PCA. The visual
emotion recognition results also show that the mainfold features from Isomap have better
performance than the original features and the manifold features from PCA.Moreover,the
recognition rates are more balanced between different emotions.