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Binarized eigenphases applied to limited memory face recognition systems

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Abstract

Most of the algorithms proposed for face recognition involve considerable amount of computations and hence they cannot be used on devices constrained with limited memory. In this paper, we propose a novel solution for efficient face recognition problem for the systems that utilize low memory devices. The new technique applies the principal component analysis to the binarized phase spectrum of the Fourier transform of the covariance matrix constructed from the MPEG-7 Fourier Feature Descriptor vectors of the images. The binarization step that is applied to the phases adds many interesting advantages to the system. It will be shown that the proposed technique increases the face recognition rate and at the same time achieves substantial savings in the computational time, when compared to other known systems. Experiments on two independent databases of face images are reported to demonstrate the effectiveness of the proposed technique.

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Acknowledgments

The authors would like to thank the reviewers for the constructive suggestions and valuable comments.

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Authors and Affiliations

  1. Electronic and Electrical Engineering Department, University of Surrey, GU2 7XH, Guildford, United Kingdom

    Naser Zaeri & Farzin Mokhtarian

  2. Electrical Engineering Department, Kuwait University, PO Box 5969, Safat, Kuwait

    Abdallah Cherri

Authors
  1. Naser Zaeri
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  2. Farzin Mokhtarian
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  3. Abdallah Cherri
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Correspondence to Naser Zaeri.

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Zaeri, N., Mokhtarian, F. & Cherri, A. Binarized eigenphases applied to limited memory face recognition systems. Pattern Anal Applic 11, 373–383 (2008). https://doi.org/10.1007/s10044-008-0129-7

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  • DOI: https://doi.org/10.1007/s10044-008-0129-7

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