inis 18(12): e3

Research Article

Comparison of Different Neural Network Training Algorithms with Application to Face Recognition

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  • @ARTICLE{10.4108/eai.10-1-2018.153550,
    author={A. Vulovic and T. Sustersic and A. Peulic and N. Filipovic and V. Rankovic},
    title={Comparison of Different Neural Network Training Algorithms with Application to Face Recognition},
    journal={EAI Endorsed Transactions on Industrial Networks and Intelligent Systems},
    volume={4},
    number={12},
    publisher={EAI},
    journal_a={INIS},
    year={2018},
    month={1},
    keywords={face recognition, neural network, Eigenface algorithm.},
    doi={10.4108/eai.10-1-2018.153550}
}
  • A. Vulovic
    T. Sustersic
    A. Peulic
    N. Filipovic
    V. Rankovic
    Year: 2018
    Comparison of Different Neural Network Training Algorithms with Application to Face Recognition
    INIS
    EAI
    DOI: 10.4108/eai.10-1-2018.153550
A. Vulovic1,2,*, T. Sustersic1,2, A. Peulic1,2, N. Filipovic1,2, V. Rankovic1,2
  • 1: Faculty of Engineering, University of Kragujevac, Sestre Janjić 6, 34 000, Kragujevac, Serbia
  • 2: Bioengineering Research and Development Center (BioIRC) Prvoslava Stojanovića 6, Kragujevac, Serbia
*Contact email: aleksandra.vulovic@kg.ac.rs

Abstract

Research in the field of face recognition has been popular for several decades. With advances in technology, approaches to solving this problems haves changed. Main goal of this paper was to compare different training algorithms for neural networks and to apply them for face recognition as it is a nonlinear problem. Algorithm that we have used for face recognition problem was the Eigenface algorithm that belongs to the Principal Component Analysis (PCA) algorithms. Percentage of recognition for all the used training functions is above 90%.

Keywords
face recognition, neural network, Eigenface algorithm.
Received
2016-12-02
Accepted
2017-12-08
Published
2018-01-10
Publisher
EAI
http://dx.doi.org/10.4108/eai.10-1-2018.153550

Copyright © 2018 Author et al., licensed to EAI. This is an open access article distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/3.0/), which permits unlimited use, distribution and reproduction in any medium so long as the original work is properly cited.