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Quaternion Principal Component Analysis for Multi-modal Fusion

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Book cover Genetic and Evolutionary Computing (GEC 2015)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 388))

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  • International Conference on Genetic and Evolutionary Computing

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Abstract

This paper proposes a multi-modal fusion method that based on quaternion, and principal component analysis (PCA) in quaternion field is involved in our algorithm. We can fuse four different features into quaternion and complete the recognition process in quaternion field. This algorithm reduces the equal error rate (EER) while fusing more kinds of features. Our experiments that fuses three kinds of modalities and four different features with two kinds of modalities respectively show a observably improvement on recognition rate with the proposed algorithm.

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Author information

Authors and Affiliations

  1. Department of Electronic Engineering, Heilongjiang University, Harbin, Heilongjiang, China

    Meng Chen, Xiao Meng & Zhifang Wang

  2. Zhengzhou Kindergartens Teachers’ College, Zhenzhou, Henan, China

    Chenxia Wang

Authors
  1. Meng Chen
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  2. Chenxia Wang
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  3. Xiao Meng
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  4. Zhifang Wang
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Corresponding author

Correspondence to Zhifang Wang .

Editor information

Editors and Affiliations

  1. Faculty of Engineering, University of Miyazaki, Miyazaki, Japan

    Thi Thi Zin

  2. School of Computer Science and Tech..., Harbin Institute of Technology, Shenzhen Graduate School , Shenzhen, China

    Jerry Chun-Wei Lin

  3. College of Information Science and Engg, Fujian University of Technology, Fuzhou, China

    Jeng-Shyang Pan

  4. Faculty of Engineering, University of Miyazaki, Miyazaki, Japan

    Pyke Tin

  5. Faculty of Engineering, University of Miyazaki, Miyazaki, Japan

    Mitsuhiro Yokota

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© 2016 Springer International Publishing Switzerland

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Chen, M., Wang, C., Meng, X., Wang, Z. (2016). Quaternion Principal Component Analysis for Multi-modal Fusion. In: Zin, T., Lin, JW., Pan, JS., Tin, P., Yokota, M. (eds) Genetic and Evolutionary Computing. GEC 2015. Advances in Intelligent Systems and Computing, vol 388. Springer, Cham. https://doi.org/10.1007/978-3-319-23207-2_2

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  • DOI: https://doi.org/10.1007/978-3-319-23207-2_2

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-23206-5

  • Online ISBN: 978-3-319-23207-2

  • eBook Packages: EngineeringEngineering (R0)

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