반도체디스플레이기술학회지 (Journal of the Semiconductor & Display Technology)

한국반도체디스플레이기술학회 (The Korean Society Of Semiconductor & Display Technology)
권호 표지

A Study of Machine Learning based Face Recognition for User Authentication

  • 투고 : 2020.06.25
  • 심사 : 2020.06.26
  • 발행 : 2020.06.30
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초록

According to brilliant development of smart devices, many related services are being devised. And, almost every service is designed to provide user-centric services based on personal information. In this situation, to prevent unintentional leakage of personal information is essential. Conventionally, ID and Password system is used for the user authentication. This is a convenient method, but it has a vulnerability that can cause problems due to information leakage. To overcome these problem, many methods related to face recognition is being researched. Through this paper, we investigated the trend of user authentication through biometrics and a representative model for face recognition techniques. One is DeepFace of FaceBook and another is FaceNet of Google. Each model is based on the concept of Deep Learning and Distance Metric Learning, respectively. And also, they are based on Convolutional Neural Network (CNN) model. In the future, further research is needed on the equipment configuration requirements for practical applications and ways to provide actual personalized services.

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참고문헌

  1. Hyung-Jin Mun, "A Study on the User Identification and Authentication in the Smart Mirror in Private", Journal of Convergence for Information Technology, vol.9, no.7, pp.100-105, 2019. https://doi.org/10.22156/CS4SMB.2019.9.7.100
  2. H. J. Moon, S. H. Kim, "Face Recognition : A Survey", Korea Information Processing Society Review, vol.20, no.3, pp.14-23, 2013.
  3. H. I. Kim, J. Y. Moon, J. Y. Park, "Research Trends for Deep Learning-Based High-Performance Face Recognition Technology", Electronics and Telecommunications Trends, vol.33, no.4, pp.43-53, 2018. https://doi.org/10.22648/ETRI.2018.J.330405
  4. H. S. Choi, Y. H. Cho, "Analysis of Security Problems of Deep Learning Technology", Journal of the Korea Convergence Society, vol.10, no.5, pp.9-16, 2019. https://doi.org/10.15207/JKCS.2019.10.5.009
  5. Artem Oppermann, "What is Deep Learning and How does it work?", (https://towardsdatascience.com/whatis-deep-learning-and-how-does-it-work-2ce44bb692ac), Nov. 2019.
  6. Y. H. Lee, Y. S. Kim, "Comparison of CNN and YOLO for Object Detection", Journal of the Semiconductor & Display Technology, vol.19, no.1, pp.85-92, 2020.
  7. Alex Krizhevsky, Ilya Sutskever, Geoffrey E. Hinton, "ImageNet Classification with Deep Convolutional Neural Networks", Communications of the ACM, vol. 60, no.6, 2017.
  8. Rahul Haridas, Jyothi RL, "Convolutional Neural Networks: A Comprehensive Survey", International Journal of Applied Engineering Research, vol.14, no.3, pp.780-789, 2019. https://doi.org/10.37622/IJAER/14.3.2019.780-789
  9. Ali Sharif Razavian, Hossein Azizpour, Josephine Sullivan, Stefan Carlsson, "CNN Features Off-the-Shelf: An Astounding Baseline for Recognition", IEEE Conference on Computer Vision and Pattern Recognition, pp.512-519, 2014.
  10. Hyochang Ahn, Yong-Hwan Lee, "A Research of CNNbased Object Detection for Multiple Object Tracking in Image", Journal of the Semiconductor & Display Technology, pp.110-114, 2019.
  11. Yaniv Taigman, Ming Yang, Marc'Aurelio, Lior Wolf, "DeepFace: Closing the Gap to Human-Level Performance in Face Verification", CVPR 2014, 2014.
  12. Jae Sung Choi, "Predictive Maintenance of the Robot Trouble Using the Machine Learning Method", Journal of the Semiconductor & Display Technology, 2020.
  13. Florian Schroff, Dmitry Kalenichenko, James Philbin, "FaceNet: A Unified Embedding for Face Recognition and Clustering", CVPR 2015, 2015.
  14. Labeled Faces in the Wild, UMass(http://vis-www.cs.umass.edu/lfw/)
  15. YouTube Faces DB, TAU(https://www.cs.tau.ac.il/-wolf/ytfaces/)