Expression Recognition in Videos Using a Weighted Component-Based Feature Descriptor

  • Xiaohua Huang
  • Guoying Zhao
  • Matti Pietikäinen
  • Wenming Zheng
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6688)

Abstract

In this paper, we propose a weighted component-based feature descriptor for expression recognition in video sequences. Firstly, we extract the texture features and structural shape features in three facial regions: mouth, cheeks and eyes of each face image. Then, we combine these extracted feature sets using confidence level strategy. Noting that for different facial components, the contributions to the expression recognition are different, we propose a method for automatically learning different weights to components via the multiple kernel learning. Experimental results on the Extended Cohn-Kanade database show that our approach combining component-based spatiotemporal features descriptor and weight learning strategy achieves better recognition performance than the state of the art methods.

Keywords

Spatiotemporal features LBP-TOP EdgeMap Information fusion Multiple kernel learning Facial expression recognition 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Xiaohua Huang
    • 1
    • 2
  • Guoying Zhao
    • 1
  • Matti Pietikäinen
    • 1
  • Wenming Zheng
    • 2
  1. 1.Machine Vision Group, Department of Electrical and Information EngineeringUniversity of OuluFinland
  2. 2.Research Center for Learning ScienceSoutheast UniversityChina

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