关键词: 局部保持投影, 有监督学习, 类间散度约束, 表情识别

Abstract: Locality Preserving Projection（LPP） algorithm is unsupervised, which does not take into account the impact on different classes of samples on the classification effect and results in the overlap of the data points for different classes, so the sub-space for classification problems may not be optimal. This paper proposes a new expression recognition algorithm based on Supervised Discriminative Locality Preserving Projection（SDLPP）. The algorithm firstly makes use of the classification information of samples to reconstruct the similarity matrix of LPP, and then adds between-class scatter constraint into the objective function. This will make sample points of different classes away from each other when preservers them in local structure, so as to get more discriminative expression feathers. The method improves recognition rate than others, and the results of the experiments on JAFFE database indicate that it is effective.

Key words: Locality Preserving Projection（LPP）, supervised learning, between-class scatter constraint, expression recognition