Expression Classification and Intensity Estimation by Expression Manifold Synthesis

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• 关键词：Expression synthesis ; Manifold ; Eigentansformation ; Expression intensity estimation
• 刊名：Lecture Notes in Computer Science
• 出版年：2016
• 出版时间：2016
• 年：2016
• 卷：9937
• 期：1
• 页码：635-644
• 全文大小：1,111 KB
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• 作者单位：Yao Peng (21)
  Hujun Yin (21)
  
  21. School of Electrical and Electronic Engineering, The University of Manchester, Manchester, M13 9PL, UK
  
• 丛书名：Intelligent Data Engineering and Automated Learning ¨C IDEAL 2016
• ISBN：978-3-319-46257-8
• 刊物类别：Computer Science
• 刊物主题：Artificial Intelligence and Robotics
  Computer Communication Networks
  Software Engineering
  Data Encryption
  Database Management
  Computation by Abstract Devices
  Algorithm Analysis and Problem Complexity
  
• 出版者：Springer Berlin / Heidelberg
• ISSN：1611-3349
• 卷排序：9937

文摘

Facial expression and its dynamic property play an important role in interpreting and conveying emotions. Recently facial expression analysis has been an active topic in both psychology and computer vision. Most previous investigations have focused on the recognition of static images with intense expressions. Different from the previous work, we present an expression synthesis method for both expression classification and intensity estimation. By means of synthesising expression manifolds from neutral faces, the dynamic variations in facial expression can be modelled and analysed. Eigentransformation is utilised on both shape and expression details in generating novel expressions. Expression classification is performed on the expanded training sets with synthesised expression landmarks, and the intensity can be estimated with synthesised expression manifolds. Comprehensive experimental results conducted on the extended Cohn-Kanade database are reported.