• 综合
  • 标题
  • 关键词
  • 摘要
  • 学者
  • 期刊-刊名
  • 期刊-ISSN
  • 会议名称
搜索

作者:

Sheng, Nan (Sheng, Nan.) | Cai, Yiheng (Cai, Yiheng.) | Zhan, Changfei (Zhan, Changfei.) | Qiu, Changyan (Qiu, Changyan.) | Cui, Yize (Cui, Yize.) | Gao, Xurong (Gao, Xurong.)

收录:

EI Scopus

摘要:

The 3D facial surface demonstrates rich information about human beings' expressions. However, methods to recognize humans' facial expression are mainly still focusing on 2D images, which is not robust to pose and lighting conditions. In this paper, the problem of the person-independent facial expression recognition is addressed on basis of the line segments connected by specific 3D automatically detected facial keypoints and LBP features of depth images around the automatically detected facial keypoints. Using a Support Vector Machine classifier, the recognition rate reaches up to 92.1% on the BU-3DFE database. Comparative analysis shows that our method outperforms the competitor approaches using similar experimental settings, which proves the effectiveness of our method for 3D facial expression recognition. © 2016 IEEE.

关键词:

Biomedical engineering Classification (of information) Face recognition Feature extraction Support vector machines

作者机构:

  • [ 1 ] [Sheng, Nan]College of Electronic and Control Engineering, Beijing University of Technology, Beijing; 100124, China
  • [ 2 ] [Cai, Yiheng]College of Electronic and Control Engineering, Beijing University of Technology, Beijing; 100124, China
  • [ 3 ] [Zhan, Changfei]College of Electronic and Control Engineering, Beijing University of Technology, Beijing; 100124, China
  • [ 4 ] [Qiu, Changyan]College of Electronic and Control Engineering, Beijing University of Technology, Beijing; 100124, China
  • [ 5 ] [Cui, Yize]College of Electronic and Control Engineering, Beijing University of Technology, Beijing; 100124, China
  • [ 6 ] [Gao, Xurong]College of Electronic and Control Engineering, Beijing University of Technology, Beijing; 100124, China

通讯作者信息:

电子邮件地址:

查看成果更多字段

相关关键词:

相关文章:

来源 :

年份: 2016

页码: 396-401

语种: 英文

被引次数:

WoS核心集被引频次: 0

SCOPUS被引频次: 4

ESI高被引论文在榜: 0 展开所有

万方被引频次:

中文被引频次:

近30日浏览量: 3

归属院系:

在线人数/总访问数:92/3604092
地址:北京工业大学图书馆(北京市朝阳区平乐园100号 邮编:100124) 联系我们:010-67392185
版权所有:北京工业大学图书馆 站点建设与维护:北京爱琴海乐之技术有限公司