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Spectral embedding based facial expression recognition with multiple features

Journal Article


Abstract


  • Many approaches to facial expression recognition utilize only one type of features at a time. It can be difficult for a single type of features to characterize in a best possible way the variations and complexity of realistic facial expressions. In this paper, we propose a spectral embedding based multi-view dimension reduction method to fuse multiple features for facial expression recognition. Facial expression features extracted from one type of expressions can be assumed to form a manifold embedded in a high dimensional feature space. We construct a neighborhood graph that encodes the structure of the manifold locally. A graph Laplacian matrix is constructed whose spectral decompositions reveal the low dimensional structure of the manifold. In order to obtain discriminative features for classification, we propose to build a neighborhood graph in a supervised manner by utilizing the label information of training data. As a result, multiple features are able to be transformed into a unified low dimensional feature space by combining the Laplacian matrix of each view with the multiview spectral embedding algorithm. A linearization method is utilized to map unseen data to the learned unified subspace. Experiments are conducted on a set of established real-world and benchmark datasets. The experimental results provide a strong support to the effectiveness of the proposed feature fusion framework on realistic facial expressions. © 2013 .

UOW Authors


  •   Yu, Kaimin (external author)
  •   Wang, Zhiyong (external author)
  •   Hagenbuchner, M.
  •   Feng, David (external author)

Publication Date


  • 2014

Citation


  • Yu, K., Wang, Z., Hagenbuchner, M. & Feng, D. (2014). Spectral embedding based facial expression recognition with multiple features. Neurocomputing, 129 136-145.

Scopus Eid


  • 2-s2.0-84893734967

Ro Metadata Url


  • http://ro.uow.edu.au/eispapers/4089

Number Of Pages


  • 9

Start Page


  • 136

End Page


  • 145

Volume


  • 129

Abstract


  • Many approaches to facial expression recognition utilize only one type of features at a time. It can be difficult for a single type of features to characterize in a best possible way the variations and complexity of realistic facial expressions. In this paper, we propose a spectral embedding based multi-view dimension reduction method to fuse multiple features for facial expression recognition. Facial expression features extracted from one type of expressions can be assumed to form a manifold embedded in a high dimensional feature space. We construct a neighborhood graph that encodes the structure of the manifold locally. A graph Laplacian matrix is constructed whose spectral decompositions reveal the low dimensional structure of the manifold. In order to obtain discriminative features for classification, we propose to build a neighborhood graph in a supervised manner by utilizing the label information of training data. As a result, multiple features are able to be transformed into a unified low dimensional feature space by combining the Laplacian matrix of each view with the multiview spectral embedding algorithm. A linearization method is utilized to map unseen data to the learned unified subspace. Experiments are conducted on a set of established real-world and benchmark datasets. The experimental results provide a strong support to the effectiveness of the proposed feature fusion framework on realistic facial expressions. © 2013 .

UOW Authors


  •   Yu, Kaimin (external author)
  •   Wang, Zhiyong (external author)
  •   Hagenbuchner, M.
  •   Feng, David (external author)

Publication Date


  • 2014

Citation


  • Yu, K., Wang, Z., Hagenbuchner, M. & Feng, D. (2014). Spectral embedding based facial expression recognition with multiple features. Neurocomputing, 129 136-145.

Scopus Eid


  • 2-s2.0-84893734967

Ro Metadata Url


  • http://ro.uow.edu.au/eispapers/4089

Number Of Pages


  • 9

Start Page


  • 136

End Page


  • 145

Volume


  • 129