Boosting graph embedding framework and its application to expression recognition
提升图嵌入框架及在表情识别中的应用 *

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.