융합정보논문지 (Journal of Convergence for Information Technology)

중소기업융합학회 (Convergence Society for SMB)
권호 표지
DOI QR코드

DOI QR Code

실시간 미니드론 카메라 영상을 기반으로 한 얼굴 인식 시스템 개발

Development of Face Recognition System based on Real-time Mini Drone Camera Images

  • Kim, Sung-Ho (Department of Computer Science and Engineering, Sangji University)
  • 투고 : 2019.10.11
  • 심사 : 2019.12.20
  • 발행 : 2019.12.28
PDF 다운로드
⟨ 이전 논문 다음 논문 ⟩

초록

본 논문에서는 미니 드론을 조종하면서 드론에 부착된 카메라가 촬영하는 영상을 실시간으로 받아들여 특정인의 얼굴을 인식하여 확인시켜주는 시스템 개발 방법론을 제안한다. 본 시스템의 개발을 위해서는 OpenCV, Python 관련 라이브러리 및 드론 SDK 등을 사용한다. 실시간 드론 영상으로부터 특정인의 얼굴 인식 비율을 높이기 위해서는 딥러닝 기반의 얼굴 인식 알고리즘을 사용하며 특히 Triples 원리를 활용한다. 시스템의 성능을 확인하기 위해 저자 얼굴을 기준으로 30회 동안 얼굴 인식 실험을 수행한 결과 약 95% 이상의 인식률을 보여주었다. 본 논문의 연구 결과물은 관광지, 축제 행사장 등에서 특정인을 드론으로 빠르게 찾기 위한 목적으로 사용할 수 있을 것으로 판단된다.

In this paper, I propose a system development methodology that accepts images taken by camera attached to drone in real time while controlling mini drone and recognize and confirm the face of certain person. For the development of this system, OpenCV, Python related libraries and the drone SDK are used. To increase face recognition ratio of certain person from real-time drone images, it uses Deep Learning-based facial recognition algorithm and uses the principle of Triples in particular. To check the performance of the system, the results of 30 experiments for face recognition based on the author's face showed a recognition rate of about 95% or higher. It is believed that research results of this paper can be used to quickly find specific person through drone at tourist sites and festival venues.

키워드

참고문헌

  1. E. H. Sun, T. H. Luat, D. Y. Kim & Y. T. Kim. (2015). A Study on the Image-based Automatic Flight Control of Mini Drone. Journal of Korean Institute of Intelligent Systems, 25(6), 536-541. https://doi.org/10.5391/JKIIS.2015.25.6.536
  2. D. W. Kim et al. (2017). AI-Based Drone Object Tracking System: Design and Implementation. The Journal of Korean Institute of Communications and Information Sciences, 42(12), 2391-2401. https://doi.org/10.7840/kics.2017.42.12.2391
  3. Zero Zero Robotics. (2018). HOVER. https://gethover.com/?d=pc&c=kr
  4. Super Data Science. (2019). Face detection using OpenCV and Python: A beginner's guide. https://www.superdatascience.com/opencv-facedetection
  5. Mike Driscoll. (2018). Face Detectioin using Python and OpenCV. https://dzone.com/articles/face-detection-usingpython-and-opencv
  6. Adrian Rosebrock. (2015). Creating a face detection API with Python and OpenCV. https://www.pyimagesearch.com/2015/05/11/creating-a-face-detection-api-with-python-and-opencv-in-just-5-minutes
  7. Adrian Rosebrock. (2018). Face recognition with OpenCV, Python and Deep learning. https://www.pyimagesearch.com/2018/06/18/face-recognition-with-opencv-python-and-deep-learning
  8. Elliot Forbes. (2017). An Introduction to Face Recognitioin in Python. https://tutorialedge.net/python/intro-face-recognition-in-python
  9. Shantnu Tiwari. (2012-2019). Face Detection in Python Using a Webcam. https://realpython.com/face-detection-in-python-using-a-webcam
  10. Ben Virdee. (2017). Face Recognition : Kairos vs Microsoft vs Google vs Amazon vs OpenCV. https://www.kairos.com/blog/face-recognition-kairos-vs-microsoft-vs-google-vs-amazon-vs-opencv
  11. Sefik Ilkin Serengil. (2018). Real Time Facial Expression Recognition on Streaming Data. https://sefiks.com/2018/01/10/real-time-facial-expression-recognition-on-streaming-data
  12. J. H. Lee. (2018). A Method of Eye and Lip Region Detection using Faster R-CNN in Face Image. Journal of the Korea Convergence Society, 9(8), 1-8. https://doi.org/10.15207/JKCS.2018.9.8.001
  13. Arun Ponnusamy. (2018). CNN based face detector from dlib. https://towardsdatascience.com/cnn-based-facedetector-from-dlib-c3696195e01c
  14. Varun Kumar. (2017). 15 Efficient Face Recognition Algorithms And Techniques. https://www.rankred.com/face-recognition-algorithms-techniques
  15. Glenn. (2018). Fast and Accurate Face Tracking in Live Video with Python. https://www.codemade.io/fast-and-accurate-face-tracking-in-live-video-with-python/
  16. M. Gemici & Y. Zhuang. (2011). Autonomous Face Detection and Human Tracking using AR Drone Quadrotor. http://www.cs.cornell.edu/courses/cs4758/2011sp/final_projects/spring_2011/Gemici_Zhuang.pdf
  17. H. K. Kim, J. M. Moon & J. Y. Park. (2018). Research Trends for Deep Learning-Based High-Performance Face Recognition Technology. ETRI Electronics and Telecommunications Trends, 33(4), 43-53.
  18. O. S. Kwon. (2018). Face recognition Based on Super-resolution Method Using Sparse Representation and Deep Learning. Journal of Korea Multimedia Society, 21(2), 173-180. https://doi.org/10.9717/kmms.2018.21.2.173
  19. W. J. Hwang. (2017). Trends in Deep Learning-based Face Detection, Landmark Detection and Face Recognition Technology. The Korean Society Of Broad Engineers, Broadcasting and Media Magazine, 22(4), 41-49.
  20. T. Y. Ko et al. (2017). Real-time face recognition and tracking system using deep learning in various environments. The Institute of Electronics and Information Engineers, 643-646.
  21. S. Y. Kang et al. (2017). Real-time Missing Persons Recognition System through CCTV based on Deep learning. Conference of the Korea Information Science Society, 1941-1943.
  22. RAZE. (2019). Tello. https://www.ryzerobotics.com/kr/tello
  23. A. Geitgey. (2016). Machine Learning is Fun! Part 4: Modern Face Recognition with Deep Learning. https://medium.com/@ageitgey/machine-learning-is-fun-part-4-modern-face-recognition-with-deep-learning-c3cffc121d78
  24. A. Geitgey. (2019). Face Recognition Documentation. https://media.readthedocs.org/pdf/face-recognition/latest/face-recognition.pdf
  25. A. Rosebrock. (2018). How to build a deep learning image dataset. https://www.pyimagesearch.com/2018/04/09/how-to-quickly-build-a-deep-learning-image-dataset/