Parallel architecture for face recognition using MPI

Dalia Shouman Ibrahim, Salma Hamdy

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The face recognition applications are widely used in different fields like security and computer vision. The recognition process should be done in real time to take fast decisions. Princi-ple Component Analysis (PCA) considered as feature extraction technique and is widely used in facial recognition applications by projecting images in new face space. PCA can reduce the dimensionality of the image. However, PCA consumes a lot of processing time due to its high intensive computation nature. Hence, this paper proposes two different parallel architectures to accelerate training and testing phases of PCA algorithm by exploiting the benefits of distributed memory architecture. The experimental results show that the proposed architectures achieve linear speed-up and system scalability on different data sizes from the Facial Recognition Technology (FERET) database.
Original languageEnglish
Article number54
Pages (from-to)425-430
Number of pages6
JournalInternational Journal of Advanced Computer Science and Applications (IJACSA)
Issue number1
Publication statusPublished - 17 Feb 2017
Externally publishedYes


  • Face recognition
  • PCA
  • MPI
  • Parallel programming
  • Distributed memory architecture


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  • Hybrid MPI/OpenMP Implementation of PCA

    Ibrahim, D. S. & Hamdy, S., 5 Dec 2017, Proceedings of the 2017 IEEE Eighth International Conference on Intelligent Computing and Information Systems (ICICIS 2017). Cairo, Egypt: IEEE , p. 205-211 7 p.

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    Open Access
    1 Citation (Scopus)
    217 Downloads (Pure)

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