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

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.

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