提升图嵌入框架及在表情识别中的应用 *

提出了一种提升图嵌入框架用于特征提取和选择 ,以及一种新的近邻权重计算方法 ,称为分类图。传统图嵌入模型的近邻权重采用欧氏距离 ,不能被提升算法所更新 ;相比较 ,分类图采用的是提升算法中样本的权重,反映的是样本在分类过程中的重要程度 ,有效地提高了图嵌入模型的分类性能。在通用人脸表情库上的识别实验结果验证了提升图嵌入模型的有效性。

Boosting graph embedding framework and its application to expression recognition

This paper proposed a boosting graph embedding framework for feature extraction and selection. Further more, pro-posed a new adjacency graph weighting method, called classification graph. Traditional graph weighting method, which was based on Euclidean distance of the samples, could not use classification information which got from boosting framework. Differ-ent from the traditional graph weighting method, classification graph was constructed using the weight of training samples. Therefore, classification graph could reflect the importance of the samples in classification, and improved the performance of the boosting graph embedding. Experimental results on Cohn-Kanade facial expression database demonstrate the effectiveness of this approach.