Face Recognition System Design and Implementation using Neural Networks

Face recognition technology is used in biometric security systems to identify a person digitally before granting the access to the system or the data in it. There are many kidnappings or abduction cases happen around us, however, the kidnap suspects will be set free if there is lack of evidence or when the victims are not able to testify in court because they suffer from post-traumatic stress disorder (PTSD). The objectives of this study are, to develop a device that will capture the image of a kidnapper as evidence for future reference and send the captured image to the family of the victim through email, to design a face recognition system to be used in searching kidnap suspects and to determine the best training parameters for the convolution neural network (CNN) layers used by the proposed face recognition system. The accuracy of the proposed system is tested with three different datasets, namely the AT&T database, face database from [23] and a custom face dataset. The results are 87.50%, 92.19% and 95.93% respectively. The overall face recognition accuracy of the proposed system is 98.48%. The best training parameters for the proposed CNN model are kernel size of 5x5, 32 and 64 filters for first and second convolutional layers and learning rate of 0.001.

@article{Alsayaydeh2022,
  title     = {Face Recognition System Design and Implementation using Neural Networks},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {13},
  number    = {6},
  year      = {2022},
  publisher = {The Science and Information Organization},
  author    = {Jamil Abedalrahim Jamil Alsayaydeh and Irianto and Azwan Aziz and Chang Kai Xin and A. K. M. Zakir Hossain and Safarudin Gazali Herawan},
  doi       = {10.14569/IJACSA.2022.0130663},
  url       = {https://doi.org/10.14569/IJACSA.2022.0130663}
}