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DOI: 10.14569/IJACSA.2018.091235
PDF

CNNSFR: A Convolutional Neural Network System for Face Detection and Recognition

Author 1: Lionel Landry SOP DEFFO
Author 2: Elie TAGNE FUTE
Author 3: Emmanuel TONYE

International Journal of Advanced Computer Science and Applications(IJACSA), Volume 9 Issue 12, 2018.

  • Abstract and Keywords
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Abstract: In recent years, face recognition has become more and more appreciated and considered as one of the most promising applications in the field of image analysis. However, the existing models have a high level of complexity, use a lot of computational resources and need a lot of time to train the model. That is why it has become a promising field of research where new methods are being proposed every day to overcome these difficulties. We propose in this paper a convolutional neural network system for face recognition with some contributions. First we propose a CRelu module, second we use the module to propose a new architecture model based on the VGG deep neural network model. Thirdly we propose a two stage training strategy improved by a large margin inner product and a small dataset and finally we propose a real time face recognition system where face detection is done by a multi-cascade convolution neural network and the recognition is done by the proposed deep convolutional neural network.

Keywords: Convolutional neural network; face recognition; VGG model; CReLU module; deep learning; architecture

Lionel Landry SOP DEFFO, Elie TAGNE FUTE and Emmanuel TONYE, “CNNSFR: A Convolutional Neural Network System for Face Detection and Recognition” International Journal of Advanced Computer Science and Applications(IJACSA), 9(12), 2018. http://dx.doi.org/10.14569/IJACSA.2018.091235

@article{DEFFO2018,
title = {CNNSFR: A Convolutional Neural Network System for Face Detection and Recognition},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2018.091235},
url = {http://dx.doi.org/10.14569/IJACSA.2018.091235},
year = {2018},
publisher = {The Science and Information Organization},
volume = {9},
number = {12},
author = {Lionel Landry SOP DEFFO and Elie TAGNE FUTE and Emmanuel TONYE}
}



Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

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