The Journal of the Korea institute of electronic communication sciences (한국전자통신학회논문지)

Korea Institute of Electronic Communication Science (한국전자통신학회)
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Face Recognition and Temperature Measurement Access Control System using Machine Learning

기계학습을 활용한 얼굴 인식 및 체온 측정 출입관리 시스템

  • 김진하 (순천대학교 컴퓨터공학과) ;
  • 김응곤 (순천대학교 컴퓨터공학과)
  • Received : 2020.12.04
  • Accepted : 2021.02.17
  • Published : 2021.02.28
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Abstract

In order to prevent the spread of COVID 19, the body temperature is measured when entering the building. In this paper, we try to certify the entry of the building through real-time face recognition based on the face learning data of visitors. The number of learning images are designed to be automatically labeled to increase facial recognition. Also, it designates the forehead region from the face region as the region of interest for accurate temperature measurements. In the future, we plan to establish a database that stores the temperature, access time, and information of visitors.

현재 코로나 19 확산 방지를 위해 건물 출입 시 체온을 측정하고 있다. 본 논문에서는 출입자의 얼굴 학습 데이터를 토대로 실시간 얼굴 인식을 통해 출입 인증을 하고자 한다. 얼굴 인식률을 증가시키기 위해 학습 이미지의 수를 자동으로 라벨링 되도록 설계하였다. 또한 정확한 온도 측정을 위해 얼굴 영역에서 이마 영역을 관심 영역으로 지정하였다. 향후에는 출입자의 체온, 출입시간, 정보 등을 저장하는 DB를 구축할 계획이다.

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References

  1. M. Lee, "Fragmentary thoughts about code of conduct and risk communication to prevent and control COVID-19 in Korea, 2020," Korean J. Health Educ Promot, vol. 37, no. 1, 2020, pp. 103-107. https://doi.org/10.14367/kjhep.2020.37.1.103
  2. H. Mun and K. Han, "Design for Access Control System based on Voice Recognition for Infectious Disease Prevention," J. of the Korea Convergence Society, vol. 39, no.7, 2016, pp. 19-24.
  3. Y. Lee, "A Semantic Diagnosis and Tracking System to Prevent the Spread of COVID-19," J. of the Korea Institute of Electronic Communication Sciences, vol. 15, no.3, 2020, pp. 611-616. https://doi.org/10.13067/JKIECS.2020.15.3.611
  4. S. Hwang, K. Kim, and K. Kim, "An Authentication Mechanism based on QR Code in Gate Control System," Proceedings of Symposium of the Korean Institute of communications and Information Sciences, Jeju-do, Korea, 2015, pp. 664-645.
  5. Y. Kim, S. Park, and D. Kim, "Research on Robust Face Recognition against Lighting Variation Using CNN," J. of the Korea Institute of Electronic Communication Sciences, vol. 12, no. 2, 2017, pp. 325-330. https://doi.org/10.13067/JKIECS.2017.12.2.325
  6. P. Viola and M. Jones, "Rapid Object Detection Using a Boosted Cascade of Simple Features," Proceeding of IEEE Computer Society Conference On Computer Vision and Pattern Recognition, vol. 1, 2001, pp. 511-518.
  7. S. Kim, "Design of Efficient Gradient Orientation Bin and Weight Calculation Circuit for HOG Feature Calculation," J. of The Institute of Electronics and Information Engineers, vol. 51, no. 11, 2015, pp. 66-72. https://doi.org/10.5573/IEIE.2014.51.11.066
  8. I. Cho and S. Yeom, "Long Distance Face Detection with Region of Interest Extraction," J. of Korea Institute of Intelligent Systems, vol. 28, no. 3, 2018, pp. 281-286. https://doi.org/10.5391/JKIIS.2018.28.3.281
  9. G. Son and J. Park, "A Study of Kernel Characteristics of CNN Deep Learning for Effective Fire Detection Based on Video," J. of the Korea Institute of Electronic Communication Sciences, vol. 13, no. 6, 2018, pp. 1257-1262. https://doi.org/10.13067/JKIECS.2018.13.6.1257
  10. M. Sandler, A. Howard, M. Zhu, A. Zhmoginov and L. Chieh "MobileNetV2: Inverted Residuals and Linear Bottlenecks," The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018, pp. 4510-4520. arXiv:1801.04381.