吉林大学学报（信息科学版）2017，Vol.35Issue(4)：384-391,8.
基于融合特征提取与LLE方法的表情识别
Expression Recognition Based on Fusion Features Extraction and LLE Method
摘要
Abstract
Feature extraction is a basis, a vital step and a major issue in facial expression recognition. To ensure that the extracted features can be more comprehensive characterization of a certain kind of expression, we present a feature extraction method based on fused geometry and local texture features. Geometric features are obtained from the feature points marked by AAM ( Active Appearance Model) algorithm, texture feature extraction is based on LBP ( Local Binary Pattern) algorithm, the dimension of fusion expression features is reduced by LLE ( Locally Linear Embedding ) algorithm. Finally, a multi-class SVM ( Support Vector Machine) is used for facial expression classification. Our method is deployed on the JAFFE and Yale data sets, the results show a recognition accuracy of 98. 57% and 91. 67% respectively, which prove the effectiveness of our proposed method.关键词
表情识别/主动表观模型/局部二值模式/局部线性嵌入/支持向量机Key words
facial expression recognition/active appearance model ( AAM )/local binary pattern ( LBP )/locally linear embedding ( LLE)/support vector machine ( SVM) .分类
信息技术与安全科学引用本文复制引用
兰兰,陈万忠,魏庭松..基于融合特征提取与LLE方法的表情识别[J].吉林大学学报（信息科学版）,2017,35(4):384-391,8.基金项目
吉林省科技发展计划自然基金资助项目(20150101191JC) （20150101191JC）
