Face Verification across Aging using Deep Learning with Histogram of Oriented Gradients

One of the complex procedures which affect man’s face shape and texture is facial aging. These changes tend to deteriorate the efficacy of systems that automatically verify faces. It seems that CNN (also known as Convolutional Neural Networks) are thought to be one of the most common deep learning approaches where multiple layers are trained robustly while maintaining the minimum number of learned parameters to improve system performance. In this paper, a deeper model of convolutional neural network is fitted with Histogram of Oriented Gradients (HOG) descriptor to handle feature extraction and classification of two face images with the age gap is proposed. Furthermore, the model has been trained and tested in the MORPH and FG-NET datasets. Experiments on FG-NET achieve a state of the arts accuracy (reaching 100%) while results on MORPH dataset have significant improvements in accuracy of 99.85%.

@article{Osman2020,
  title     = {Face Verification across Aging using Deep Learning with Histogram of Oriented Gradients},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {11},
  number    = {10},
  year      = {2020},
  publisher = {The Science and Information Organization},
  author    = {Areeg Mohammed Osman and Serestina Viriri},
  doi       = {10.14569/IJACSA.2020.0111084},
  url       = {https://doi.org/10.14569/IJACSA.2020.0111084}
}