计算机工程与应用2016，Vol.52Issue(20)：145-148,226,5.DOI:10.3778/j.issn.1002-8331.1411-0344
基于张量分析的表情特征提取
Facial expression feature extraction based on tensor analysis
摘要
Abstract
Facial expression feature extraction plays an important role in facial expression recognition. The expression feature extracted by existing methods is the combination of individual facial feature and expression feature. Facial recogni-tion is based on different individual facial feature, but facial expression recognition needs to find out the differences of different expressions. What is more important is individual difference will influence the facial expression recognition, and obstruct the expression reorganization rate. In an optimal situation, the related individual facial feature can be separated during the process of facial expression recognition. This paper presents a method that can eliminate interference of facial features when recognizing the facial expression. Firstly, a three order tensor will be built. Secondly, it uses the tensor analysis method to decompose the face feature and the expression feature into the person subspace and the expression subspace respectively. This method can ensure that parameters of expression and face are not related. The evaluation experiment on JAFFE proves the validity of the method.关键词
表情特征提取/表情识别/情感识别/张量分析Key words
expression feature extraction/expression recognition/affective sate recognition/tensor analysis分类
信息技术与安全科学引用本文复制引用
孙波,刘永娜,罗继鸿,张迪,张树玲,陈玖冰..基于张量分析的表情特征提取[J].计算机工程与应用,2016,52(20):145-148,226,5.基金项目
北京自然科学基金（No.4102030）；中央高校基本科研业务费专项资金资助项目（No.2014KJJCA15）；教育科学十二五规划课题（No.DCA140229）。 （）
