基于表情相似性的人脸表情流形

在图嵌入(graph embedding)的框架下提出一种根据表情相似度构建邻接权重图的方法来学习人脸表情子空间.数据集中人脸图像的表情以半监督-学习的方式来估计,人脸图像之间的表情相似性由表情模糊隶属度矢量之间的内积来度量,与个体、光照、姿态等人脸差异无关.在得到的子空间内,相似表情的人脸图像位于流形上的邻近位置,表情数据在子空间内按语义的分布很好地揭示了表情模糊、演变的特性.在Cohn-Kanade人脸表情数据库和实验室自行采集的人脸表情数据集上的实验结果说明了该方法的有效性.因此,该方法可以很好地应用于各种基于人脸表情识别的人机交互中.

Facial Expression Manifold Based on Expression Similarity

A strategy is proposed for facial expression recognition under the graph embedding (GE) framework. The neighborhood weighted graph based on the expression similarity is constructed to learn the sub-space. In the sub-space, the data distribute on the manifold based on expression semantic. The proposed sub-space method can overcome the difficulties for facial expression recognition caused by the differences in individuals, lightings, poses. The expressions of the facial images in the data set are exploited in a semi-supervised way. Expression similarity between two facial images is measured by the dot product of the expression fuzzy membership function vectors. Experimental results on Cohn-Kanade and the data set of this paper demonstrate the effectiveness of the approach.