Facial Recognition Using a Lightweight Deep Neural Networks

  • Authors

    • Jonathan Hiebert Southern Connecticut State University
    • Feezan Mazhar Southern Connecticut State University
    • Micahl Derosa Southern Connecticut State University
    • Alaa Sheta Southern Connecticut State University
  • Facial Recognition, Deep Learning, Convolutional Neural Network
  • Current facial recognition systems are still far away from the capability of the human’s face perception. Facial recognition systems can continue to be improved as technology evolves. The task of face recognition has been heavily explored in recent years. In this research, we provide our initial idea in developing Lightweight Deep Neural Networks for facial recognition. Although our goal was to create an optimal model that would exceed current facial recognition model performance, we could experiment and discover alternative approaches to multi-class facial recognition/classification. We tested with a dataset of 2800 images of men and women with specified image sizes. We created three CNN with various architectures, which we used to train with the chosen dataset for 20, 50, 100, and 200 classes per model. The experimental results exhibit the challenges of increasing the complexity of neural networks. From these results, we concluded that a Light CNN Model with a small number of layers had an average test accuracy of 94.19%, which was the best classification performance on unseen data.

  • References

      1. G. H. Bower and M. B. Karlin, “Depth of processing pictures of faces and recognition memory,†Journal of Experimental Psychology, vol. 103, pp. 751–757, 1974. [Online]. Available: https://doi.org/10.1037/h0037190
      2. H. Xu, X. Su, Y. Wang, H. Cai, K. Cui, and X. Chen, “Automatic bridge crack detection using a convolutional neural network,†Applied Sciences, vol. 9, no. 14, p. 2867, 2019.
      3. E. Florio. (2018) At Marriott, you can now check in with your face. [Online]. Available: https://www.cntraveler.com/story/marriott-alibaba-facial-recognition-hotel-check-in
      4. C. Clifford. (2018) You can pay for your burger with your face at this fast-food restaurant, thanks to a.i. [Online]. Available: https://www.cnbc.com/2018/02/02/pay-with-facial-recognition-a-i-at-caliburger-in-pasadena-california.html
      5. G. Barkho. (2019) Walmart confirms the use of ai-powered cameras to detect stealing. [Online]. Available: https://observer.com/2019/06/walmart-ai-cameras-detect-stealing/
      6. Apple Press Release. (2017) The future is here: iphone x. [Online]. Available: https://www.apple.com/newsroom/2017/09/ the-future-is-here-iphone-x/
      7. [7] S. O’Dea. (2021) Share of smartphone users that use an Apple iPhone in the United States from 2014 to 2021. [Online]. Available: https://www.statista.com/statistics/236550/percentage-of-us-population-that-own-a-iphone-smartphone/
      8. J.-C. Chen, R. Ranjan, S. Sankaranarayanan, A. Kumar, C.-H. Chen, V. M. Patel, C. D. Castillo, and R. Chellappa, “Unconstrained still/video-based face verification with deep convolutional neural networks,†2017.
      9. D. S. Abdelminaam, A. M. Almansori, M. Taha, and E. Badr, “A deep facial recognition system using computational intelligent algorithms,†PLOS ONE, vol. 15, no. 12.
      10. Grand View Research. (2020) Facial recognition market size, share trends analysis report by technology (2d, 3d), by application (emotion recognition, attendance tracking monitoring), by end-use, and segment forecasts, 2020 - 2027. [Online]. Available: https://www.grandviewresearch.com/industry-analysis/facial-recognition-market
      11. D. Castro and M. McLaughlin, “Survey: Few Americans want government to limit use of facial recognition technology, particularly for public safety or airport screening,†2019.
      12. [12] P. J. Grother, P. J. Grother, and M. Ngan, Face recognition vendor test (frvt). US Department of Commerce, National Institute of Standards and Technology, 2014.
      13. W. Crumpler, “How accurate are facial recognition systems—and why does it matter,†Center for Strategic and International Studies, vol. 14, 2020.
      14. S. Saha, “A comprehensive guide to convolutional neural networks-the eli5 way,†Dec 2018. [Online]. Available: https://towardsdatascience.com/a-comprehensive-guide-to-convolutional-neural-networks-the-eli5-way-3bd2b1164a53
      15. W. You, C. Shen, X. Guo, X. Jiang, J. Shi, and Z. Zhu, “A hybrid technique based on convolutional neural network and support vector regression for intelligent diagnosis of rotating machinery,†Advances in Mechanical Engineering, vol. 9, no. 6, 2017.
      16. G. Guo and N. Zhang, “A survey on deep learning-based face recognition,†Computer Vision and Image Understanding, vol. 189, p. 102805, 2019.
      17. S. Almabdy and L. Elrefaei, “Deep convolutional neural network-based approaches for face recognition,†Applied Sciences, vol. 9, no. 20, 2019.
      18. M. Wang and W. Deng, “Deep face recognition: A survey,†Neurocomputing, vol. 429, p. 215–244, Mar 2021. [Online]. Available: http://dx.doi.org/10.1016/j.neucom.2020.10.081
      19. V. Sharma and P. C. says:, “Role of convolutional layer in convolutional neural networks,†Oct 2020. [Online]. Available: https://vinodsblog.com/2020/05/03/role-of-convolutional-layer-in-cnn/#:~:text=ConvolutionalLayerT1 textendashAnOutlook,i.e.extractfeaturesfromit.
      20. S. Khosla. (2019) Cnn | introduction to pooling layer. [Online]. Available: https://www.geeksforgeeks.org/cnn-introduction-to-pooling-layer/
      21. Deeplizard. (2017) Accuracy, precision, recall or f1? [Online]. Available: https://deeplizard.com/learn/video/m0pIlLfpXWE
      22. S. Kansal. (2017) A quick guide to activation functions in deep learning. [Online]. Available: https://towardsdatascience.com/a-quick-guide-to-activation-functions-in-deep-learning-4042e7addd5b
      23. Deshpande, “A beginner’s guide to understanding convolutional neural networks. 2016,†URl: adeshpande3. github.io/adeshpande3. github. io/A-Beginner’s-Guide-To-Understanding-Convolutional-Neural-Networks, 2020.
      24. K. P. Shung. (2018) Accuracy, precision, recall or f1? [Online]. Available: https://towardsdatascience.com/accuracy-precision-recall-or-f1-331fb37c5cb9
      25. W. Koehrsen. (2018) Beyond accuracy: Precision and recall. [Online]. Available: https://towardsdatascience.com/beyond-accuracy-precision-and-recall-3da06bea9f6c
      26. C. E. Thomaz, “Fei face database.†[Online]. Available: https://fei.edu.br/~cet/facedatabase.html
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    Hiebert, J., Mazhar, F., Derosa, M., & Sheta, A. (2021). Facial Recognition Using a Lightweight Deep Neural Networks. Journal of Advanced Computer Science & Technology, 10(1), 1-8. https://doi.org/10.14419/jacst.v10i1.31632