基于特征块主成分分析的人脸表情识别  

Facial expression recognition based on feature block principal component analysis

作　　者：李俊华[1] 彭力[1] 

机构地区：[1]江南大学通信与控制工程学院,江苏无锡214122

出　　处：《计算机工程与设计》2008年第12期3151-3153,共3页Computer Engineering and Design

摘　　要：使用特征块主成分分析(FBPCA)对人脸表情进行特征提取,然后输入最近邻分类器得到分类结果。同时,讨论了特征脸个数在实验中的影响,总结出了识别率与特征脸数量的关系。分别在JAFFE表情库上做了训练数据包含测试数据和训练数据不包含测试数据等情况的表情识别。The method of featttre vector can be obtained by feature block principal component analysis, and recognition results is output by the nearest-neighbor. At the same time, the influence of eigenface is discussed in the experiment and the relation of recognition rate and eigenface is given. The experiments of training data includes test data or not are tested in the Japanese female facial expression database.

关 键 词：特征块主成分分析 表情识别 特征脸 特征提取 最近邻分类器 识别率 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]