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Head Pose Estimation Based on Tensor Factorization

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Neural Information Processing (ICONIP 2007)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 4984))

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Abstract

This paper investigates head pose estimation problem which is considered as front-end preprocessing for improving multi-view human face recognition. We propose a computational model for perceiving head poses based on the Non-negative Multi-way Factorization (NMWF). The model consists of three components: the tensor representation for multi-view faces, feature selection and head pose perception. To find the facial representation basis, the NMWF algorithm is applied to the training data of facial images. The face tensor includes three factors of facial images, poses, and people. The former two factors are used to construct the computational model for pose estimation. The discriminative measure for perceiving the head pose is defined by the similarity between tensor basis and the representation of testing facial image which is projection of faces on the subspace spanned by the basis “TensorFaces”. Computer simulation results show that the proposed model achieved satisfactory accuracy for estimating head poses of facial images in the CAS-PEAL face database.

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Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, Shanghai Jiaotong University, 800 Dongchuan Rd., Shanghai, 200240, China

    Wenlu Yang, Liqing Zhang & Wenjun Zhu

  2. Department of Electronic Engineering, Shanghai Maritime University, 1550 Pudong Rd., Shanghai, 200135, China

    Wenlu Yang

Authors
  1. Wenlu Yang
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  2. Liqing Zhang
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  3. Wenjun Zhu
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Editor information

Masumi Ishikawa Kenji Doya Hiroyuki Miyamoto Takeshi Yamakawa

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© 2008 Springer-Verlag Berlin Heidelberg

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Yang, W., Zhang, L., Zhu, W. (2008). Head Pose Estimation Based on Tensor Factorization. In: Ishikawa, M., Doya, K., Miyamoto, H., Yamakawa, T. (eds) Neural Information Processing. ICONIP 2007. Lecture Notes in Computer Science, vol 4984. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-69158-7_86

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  • DOI: https://doi.org/10.1007/978-3-540-69158-7_86

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-69154-9

  • Online ISBN: 978-3-540-69158-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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