| 基于光流特征与高斯LDA的面部表情识别算法 |
| |
| 引用本文： | 刘涛,周先春,严锡君. 基于光流特征与高斯LDA的面部表情识别算法[J]. 计算机科学, 2018, 45(10): 286-290, 319 |
| |
| 作者姓名： | 刘涛 周先春 严锡君 |
| |
| 作者单位： | 江苏开放大学信息与机电工程学院 南京210017,南京信息工程大学电子与信息工程学院 南京210044,河海大学计算机与信息学院 南京210098 |
| |
| 基金项目： | 本文受国家自然科学基金项目(11202106,4), 江苏省高校自然科学研究面上基金项目(15KJD520003)资助 |
| |
| 摘 要： | 文中提出了一种人脸表情识别的新方法,该方法采用动态的光流特征来描述人脸表情的变化差异,提高人脸表情的识别率。首先,计算人脸表情图像与中性表情图像之间的光流特征；然后,对传统的线性判断分析方法(Linear Discriminant Analysis,LDA)进行扩展,采用高斯LDA方法对光流特征进行映射,从而得到人脸表情图像的特征向量；最后,设计多类支持向量机分类器,实现人脸表情的分类与识别。在JAFFE和CK人脸表情数据库上的表情识别实验结果表明,该方法的平均识别率比3种对比方法的高出2%以上。
|
| 关 键 词： | 表情识别 光流 线性判断分析 支持向量机 高斯分布 |
| 收稿时间： | 2017-08-08 |
| 修稿时间： | 2017-11-21 |
LDA Facial Expression Recognition Algorithm Combining Optical Flow Characteristics with Gaussian |
| |
| LIU Tao,ZHOU Xian-chun and YAN Xi-jun. LDA Facial Expression Recognition Algorithm Combining Optical Flow Characteristics with Gaussian[J]. Computer Science, 2018, 45(10): 286-290, 319 |
| |
| Authors: | LIU Tao ZHOU Xian-chun YAN Xi-jun |
| |
| Affiliation: | School of Information Mechanical & Electrical Engineering,Jiangsu Open University,Nanjing 210017,China,School of Electronic and Information Engineering,Nanjing University of Information Science & Technology,Nanjing 210044,China and College of Computer and Information,Hohai University,Nanjing 210098,China |
| |
| Abstract: | This paper presented a new method for facial expression recognition,which uses dynamic optical flow features to describe the differences in facial expressions and improve the recognition rate of facial expression recognition.Firstly,the optical flow features between a peak emotion image and the neutral expression image are calculated.Then,the linear discriminant analysis (LDA) method is extended,and the Gaussian LDA method is used to map the optical flow features into eigenvector of facial expression image.Finally,multi-class support vector machine classifier is designed to achieve the classification and the recognition of facial expression.The experimental results on the JAFFE and CK facial expression databases show that the average recognition rates of the proposed method are more than 2% higher than three benchmark methods. |
| |
| Keywords: | Expression recognition Optical flow Linear discriminant analysis Support vector machines Gaussian distribution |
|
| 点击此处可从《计算机科学》浏览原始摘要信息 |
|
点击此处可从《计算机科学》下载全文 |
|
