Abstract: Abstract: Acocording to the Fisher Linear Discriminant (FLD) extracting feature , this paper recognizes expression with Support Vector Machines (SVM) and the multi-class classification problem is solved by the approach of one-against-one. Experiments of human who participates in test has been trained or not are performed on the JAFFE database, and compared to the nearest classifier, the SVM can get better recognition ratio. So, it is feasible to apply SVM to expression recognition.

摘要： 摘 要 本文通Fisher’s Linear Discriminant(FLD)提取静态人脸表情特征，采用“一对一”支持向量机分类器进行了多种表情识别。在JAFFE人脸表情库上分别进行了测试人参与训练和不参与训练两种方案仿真实验，并与最近邻分类器进行比较，支持向量机都取得了更好的识别结果，说明了支持向量机分类器应用于表情识别是可行的