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DOI: 10.14569/IJACSA.2020.0111084
PDF

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

Author 1: Areeg Mohammed Osman
Author 2: Serestina Viriri

International Journal of Advanced Computer Science and Applications(IJACSA), Volume 11 Issue 10, 2020.

  • Abstract and Keywords
  • How to Cite this Article
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Abstract: 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%.

Keywords: Facial aging; verify faces; Convolutional Neural Networks (CNN); Histogram of Oriented Gradients (HOG)

Areeg Mohammed Osman and Serestina Viriri, “Face Verification across Aging using Deep Learning with Histogram of Oriented Gradients” International Journal of Advanced Computer Science and Applications(IJACSA), 11(10), 2020. http://dx.doi.org/10.14569/IJACSA.2020.0111084

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



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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