Paper Title:
Facial Expression Recognition Based on Features Fusion
  Abstract

In this paper, we propose a novel algorithm for facial recognition based on features fusion in support vector machine (SVM). First, some local features and global features from pre-processed face images are obtained. The global features are obtained by making use of singular value decomposition (SVD). At the same time, the local features are obtained by utilizing principal component analysis (PCA) to extract the principal Gabor features. Finally, the feature vectors which are fused with global and local features are used to train SVM to realize the face expression recognition, and the computer simulation illustrates the effectivity of this method on the JAFFE database.

  Info
Periodical
Edited by
Qi Luo
Pages
1253-1259
DOI
10.4028/www.scientific.net/AMM.20-23.1253
Citation
C. J. Zhou, X. P. Wei, Q. Zhang, "Facial Expression Recognition Based on Features Fusion", Applied Mechanics and Materials, Vols. 20-23, pp. 1253-1259, 2010
Online since
January 2010
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Price
$32.00
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