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High Performance and Efficient Facial Recognition Using Norm of ICA/Multiwavelet Features

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Advances in Visual Computing (ISVC 2015)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 9475))

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Abstract

In this paper, a supervised facial recognition system is proposed. For feature extraction, a Two-Dimensional Discrete Multiwavelet Transform (2D DMWT) is applied to the training databases to compress the data and extract useful information from the face images. Then, a Two-Dimensional Fast Independent Component Analysis (2D FastICA) is applied to different combinations of poses corresponding to the subimages of the low-low frequency subband of the MWT, and the \(\ell _2\)-norm of the resulting features are computed to obtain discriminating and independent features, while achieving significant dimensionality reduction. The compact features are fed to a Neural Network (NNT) based classifier to identify the unknown images. The proposed techniques are evaluated using three different databases, namely, ORL, YALE, and FERET. The recognition rates are measured using K-fold Cross Validation. The proposed approach is shown to yield significant improvement in storage requirements, computational complexity, as well as recognition rates over existing approaches.

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Acknowledgement

This work was supported in part by NSF grant (CCF - 1320547) and by the Iraqi government scholarship (HCED).

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Authors and Affiliations

  1. Department of Electrical and Computer Engineering, University of Central Florida, Orlando, FL, USA

    Ahmed Aldhahab, George Atia & Wasfy B. Mikhael

  2. Department of Electrical Engineering, University of Babylon, Hilla, Babylon, Iraq

    Ahmed Aldhahab

Authors
  1. Ahmed Aldhahab
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  2. George Atia
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  3. Wasfy B. Mikhael
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Corresponding author

Correspondence to Ahmed Aldhahab .

Editor information

Editors and Affiliations

  1. University of Nevada, Reno, Nevada, USA

    George Bebis

  2. NASA Ames Research Center, Moffett Field, California, USA

    Richard Boyle

  3. Lawrence Berkeley National Laboratory, Berkeley, California, USA

    Bahram Parvin

  4. Desert Research Institute, Reno, Nevada, USA

    Darko Koracin

  5. University of Houston, Houston, Texas, USA

    Ioannis Pavlidis

  6. IBM T.J. Watson Research Center, Yorktown Heights, New York, USA

    Rogerio Feris

  7. Purdue University, West Lafayette, Indiana, USA

    Tim McGraw

  8. Side Effects Software, Santa Monica, California, USA

    Mark Elendt

  9. The DiVE, Durham, North Carolina, USA

    Regis Kopper

  10. Texas A&M University, College Station, Texas, USA

    Eric Ragan

  11. Kent State University, Kent, Ohio, USA

    Zhao Ye

  12. Lawrence Berkeley National Laboratory, Berkeley, California, USA

    Gunther Weber

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Aldhahab, A., Atia, G., Mikhael, W.B. (2015). High Performance and Efficient Facial Recognition Using Norm of ICA/Multiwavelet Features. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2015. Lecture Notes in Computer Science(), vol 9475. Springer, Cham. https://doi.org/10.1007/978-3-319-27863-6_63

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  • DOI: https://doi.org/10.1007/978-3-319-27863-6_63

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-27862-9

  • Online ISBN: 978-3-319-27863-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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