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.

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}
}