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Multimodal Facial Expression Recognition Based on 3D Face Reconstruction from 2D Images

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Face and Facial Expression Recognition from Real World Videos (FFER 2014)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 8912))

Abstract

In this paper, a novel feature extraction method was proposed for facial expression recognition. A 3D Facial Expression Generic Elastic Model (3D FE-GEM) was proposed to reconstruct an expression-invariant 3D model of each human face in the present database using only a single 2D frontal image with/without facial expressions. Then, the texture and depth of the face were extracted from the reconstructed model. Afterwards, the Gabor filter bank was applied to both texture and reconstructed depth of the face to extract the feature vectors from both texture and reconstructed depth images. Finally, by combining 2D and 3D feature vectors, the final feature vectors are generated and classified by the Support Vector Machine (SVM). Favorable outcomes were acquired for facial expression recognition on the available image database based on the proposed method compared to several state-of-the-arts in facial expression recognition.

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Author information

Authors and Affiliations

  1. Amirkabir University of Technology, Tehran, Iran

    Ali Moeini

  2. Semnan University, Semnan, Iran

    Hossein Moeini

Authors
  1. Ali Moeini
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  2. Hossein Moeini
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Corresponding author

Correspondence to Ali Moeini .

Editor information

Editors and Affiliations

  1. Computer and Systems Engineering, Pensselaer Polytechnic Institute, Troy, New York, USA

    Qiang Ji

  2. Department of Architecture, Design, and Media Technology, Aalborg, Denmark

    Thomas B. Moeslund

  3. Department of Computer Science, Stevens Institute of Technology, Hoboken, New Jersey, USA

    Gang Hua

  4. Department of Architecture, Design, and Media Technology, Aalborg, Denmark

    Kamal Nasrollahi

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Moeini, A., Moeini, H. (2015). Multimodal Facial Expression Recognition Based on 3D Face Reconstruction from 2D Images. In: Ji, Q., B. Moeslund, T., Hua, G., Nasrollahi, K. (eds) Face and Facial Expression Recognition from Real World Videos. FFER 2014. Lecture Notes in Computer Science(), vol 8912. Springer, Cham. https://doi.org/10.1007/978-3-319-13737-7_5

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  • DOI: https://doi.org/10.1007/978-3-319-13737-7_5

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-13736-0

  • Online ISBN: 978-3-319-13737-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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