摘要： 人脸姿态表情变化是影响人脸识别的重要因素，传统方法主要从像素角度对人脸姿态表情进行分析。根据姿态表情的拓扑结构分析人脸姿态表情，应用非线性降维方法将高维图像数据嵌入到低维空间。该算法表示了不同姿态表情的非线性结构，具有平移、旋转等不变特性。实验证明，该方法能有效地表征人脸姿态表情的细微变化，不同人脸姿态表情在低维空间的分布是一致的，通过其邻域脸谱图像可以精确重构原来的脸谱图像。

关键词: 人脸姿态表情, 高维数据, 低维数据

Abstract: The variety of facial pose and expression will affect the result of facial recognition. The traditional methods analyses facial pose and expression from every pixel of image on the contrary. This paper analyses the pose and expression from geometric relationship among image data. It uses Locally Linear Embedding(LLE) which computes low dimensional, neighborhood preserving embedding of high-dimensional data. The algorithm finds the nonlinear structure of data effectively, invariable to translation, revolution and so on. Experiment shows that the algorithm has capability to reflect the slight variety of facial pose and expression, and indicates that the low-dimensional distribution of various pose and expression is identical to different persons and the original image can be reconstructed exactly from neighboring images.

Key words: facial pose and expression, high-dimensional data, low-dimensional data

中图分类号: 

• TP391