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Symmetry Based 2D Singular Value Decomposition for Face Recognition

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Digital Information Processing and Communications (ICDIPC 2011)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 188))

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Abstract

Two-dimensional singular value decomposition (2DSVD) is latterly presented attempt to preserve the local nature of 2D face images, and at same time alleviate the computational complexity in standard singular value decomposition (1DSVD). Human face symmetry is also a profitable natural property of face images and allowed better feature extraction capability in face recognition. This paper introduces new method for face recognition coined as symmetry based two-dimensional singular value decomposition (S2DSVD), which relies on the strengths of both 2DSVD and human face symmetry. The proposed method offers two significant advantages over 2DSVD: improves the stability of feature extraction, and increases the valuable discriminative information, hence raising recognition accuracy. S2DSVD is compared to both 1DSVD and 2DSVD on two well-known databases. Experimental results show improvement in recognition accuracy over 2DSVD and superior to 1DSVD.

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Author information

Authors and Affiliations

  1. Department of Computer Communications Engineering, College of Computer Engineering and Sciences, Gulf University, Sanad, Kingdom of Bahrain

    Falah E. Alsaqre & Saja Al-Rawi

Authors
  1. Falah E. Alsaqre
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  2. Saja Al-Rawi
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Editors and Affiliations

  1. Faculty of Electrical Engineering and Computer Science, VSB-Technical University of Ostrava, VŠB-TUO, 17. listopadu 15, 708 33, Ostrava-Poruba, Czech Republic

    Vaclav Snasel  & Jan Platos  & 

  2. Information Systems Department, King Saud University, 11543, Riyadh, Saudi Arabia

    Eyas El-Qawasmeh

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Alsaqre, F.E., Al-Rawi, S. (2011). Symmetry Based 2D Singular Value Decomposition for Face Recognition. In: Snasel, V., Platos, J., El-Qawasmeh, E. (eds) Digital Information Processing and Communications. ICDIPC 2011. Communications in Computer and Information Science, vol 188. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-22389-1_43

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  • DOI: https://doi.org/10.1007/978-3-642-22389-1_43

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-22388-4

  • Online ISBN: 978-3-642-22389-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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