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Facial Expression Recognition Based on BoostingTree

  • Ning Sun
  • Wenming Zheng
  • Changyin Sun
  • Cairong Zou
  • Li Zhao
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3972)

Abstract

In recent years, facial expression recognition has become an active research area that finds potential applications in the fields such as images processing and pattern recognition, and it plays a very important role in the applications of human-computer interfaces and human emotion analysis. This paper proposes an algorithm called BoostingTree, which is based on the conventional Adaboost and uses tree-structure to convert seven facial expressions to six binary problems, and also presents a novel method to compute projection matrix based on Principal Component Analysis (PCA). In this novel method, a block-merger combination is designed to solve the “data disaster” problem due to the combination of eigenvectors. In the experiment, we construct the weak classifiers set based on this novel method. The weak classifiers selected from the above set by Adaboost are combined into strong classifier to be as node classifier of one level of the tree structure. N-level tree structure built by BoostingTree can effectively solve multiclass problem such as facial expression recognition

Keywords

Facial Expression Project Matrix Facial Expression Recognition Weak Classifier Principal Component Analysis Method 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Ning Sun
    • 1
    • 2
  • Wenming Zheng
    • 1
  • Changyin Sun
    • 3
  • Cairong Zou
    • 2
  • Li Zhao
    • 1
    • 2
  1. 1.Research Center of Learning ScienceSoutheast UniversityNanjingChina
  2. 2.Department of Radio EngineeringSoutheast UniversityNanjingChina
  3. 3.College of Electrical EngineeringHohai UniversityNanjing, JiangsuChina

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