International journal of advanced smart convergence

The Institute of Internet, Broadcasting and Communication (한국인터넷방송통신학회)
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Implementation of Face Recognition Applications for Factory Work Management

  • Rho, Jungkyu (Dept. of Computer Science, Seokyeong University) ;
  • Shin, Woochang (Dept. of Computer Science, Seokyeong University)
  • Received : 2020.08.17
  • Accepted : 2020.08.28
  • Published : 2020.09.30
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Abstract

Facial recognition is a biometric technology that is used in various fields such as user authentication and identification of human characteristics. Face recognition applications are practically used in various fields, but very few applications have been developed to improve the factory work environment. We implemented applications that uses face recognition to identify a specific employee in a factory .work environment and provide customized information for each employee. Factory workers need documents describing the work in order to do their assigned work. Factory managers can use our application to register documents needed for each worker, and workers can view the documents assigned to them. Each worker is identified using face recognition, and by tracking the worker's face during work, it is possible to know that the worker is in the workplace. In addition, as a mobile app for workers is provided, workers can view the contents using a tablet, and we have defined a simple communication protocol to exchange information between our applications. We demonstrated the applications in a factory work environment and found several improvements were required for practical use. We expect these results can be used to improve factory work environments.

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References

  1. S-.H. Jang and J. Jeong, "Design and Implementation of OpenCV-based Inventory Management System to build Small and Medium Enterprise Smart Factory," The Journal of The Institute of Internet, Broadcasting and Communication (JIIBC), Vol. 19, No. 1, pp.161-170, Feb. 2019. DOI: https://doi.org/10.7236/JIIBC.2019.19.1.161
  2. OpenCV (Open Source Computer Vision Library). https://opencv.org/.
  3. E Gumus, N Kilic, A Sertbas, and O. N. Ucan, "Evaluation of face recognition techniques using PCA, wavelets and SVM," Expert Systems with Applications, Vol. 37, Issue 9, pp. 6404-6408, Sep. 2010. DOI: https://doi.org/10.1016/j.eswa.2010.02.079
  4. K. Grm, V. Struc, A. Artiges, M. Caron, and H. K. Ekenel, "Strengths and weaknesses of deep learning models for face recognition against image degradations," IET Biometrics, Vol. 7, Issue 1, pp. 81-89, Jan. 2018. DOI: https://doi.org/10.1049/iet-bmt.2017.0083
  5. T. Bourlai and B. Cukic, "Multi-spectral face recognition: Identification of people in difficult environments," in Proc. 2012 IEEE International Conference on Intelligence and Security Informatics, pp. 196-201, June 2012. DOI: https://doi.org/10.1109/ISI.2012.6284307
  6. M. Zulfiqar, F. Syed, M. J. Khan, and K. Khurshid, "Deep Face Recognition for Biometric Authentication," in Proc. 2019 International Conference on Electrical, Communication, and Computer Engineering, pp. 1-6, July 2019. DOI: https://doi.org/10.1109/ICECCE47252.2019.8940725
  7. M. A. Dabbah, W. L. Woo, and S. S. Dlay, "Secure Authentication for Face Recognition," in Proc. IEEE Symposium on Computational Intelligence in Image and Signal Processing, pp. 121-126, Apr. 2007. DOI: https://doi.org/10.1109/CIISP.2007.369304
  8. A. Okumura, S. Handa, T. Hoshino, N. Tokunaga, and M. Kanda, "Improving Face Recognition for Identity Verification by Managing Facial Directions and Eye Contact of Event Attendees," Journal of Information Processing Vol. 28, pp. 343-353, June 2020. DOI: https://doi.org/10.2197/ipsjjip.28.343
  9. Y.H. Hong, S.J. Song, and J. Rho, “Real-time Tracking and Identification for Muli-Camera Surveillance System,” International Journal of the Internet, Broadcasting and Communication (IJIBC), Vol. 10, No. 1, pp. 16-22, Feb. 2018. DOI: https://doi.org/10.7236/IJIBC.2018.10.1.3
  10. M. Sajjad, M. Nasir, K. Muhammad, S. Khan, Z. Jan, A. K. Sangaiah, M. Elhoseny, and S. W. Baik, "Raspberry Pi assisted face recognition framework for enhanced law-enforcement services in smart cities," Future Generation Computer Systems, Vol. 108, pp. 995-1007, July 2020. DOI: https://doi.org/10.1016/j.future.2017.11.013
  11. Y.H. Hong, S.J. Song, K.M. Jang, and J. Rho, “Smart Factory Platform based on Multi-Touch and Image Recognition Technologies,” The Journal of the Institute of Internet, Broadcasting and Communication (JIIBC), Vol. 18, No. 1, pp. 23-28, Feb. 2018. DOI: https://doi.org/10.7236/JIIBC.2018.18.1.23