Journal of Korea Game Society (한국게임학회 논문지)

Korea Game Society (한국게임학회)
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Implementation of Immersive Interactive Content Using Face Recognition Technology - (Exhibition of ReneMagritte) Focused on 'ARPhotoZone'

얼굴 인식 기술을 활용한 실감형 인터랙티브 콘텐츠의 구현 - (르네마그리트 특별전) AR포토존을 중심으로

  • 이은진 (숭실대학교 글로벌미디어학부) ;
  • 성정환 (숭실대학교 글로벌미디어학부)
  • Received : 2020.07.15
  • Accepted : 2020.10.07
  • Published : 2020.10.20
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Abstract

Biometric technology with the advance of deep learning enabled the new types of content. Especially, face recognition can provide immersion in terms of convenience and non-compulsiveness, but most commercial content has limitations that are limited to application areas. In this paper, we attempted to overcome these limitations, implement content that can utilize face recognition technology based on realtime video feed. We used Unity engine for high quality graphics, but performance degradation and frame drop occurred. To solve them, we augmented Dlib toolkit and adjusted the resolution image.

딥러닝의 발전에 따른 생체 인식 기술은 새로운 형태의 콘텐츠를 생산해 낼 수 있게 하였다. 특히 얼굴 인식 기술의 경우 편의성·비강제성 면에서 몰입감을 줄 수 있지만, 대부분의 상용 콘텐츠는 어플리케이션 영역에만 그치는 한계성을 가진다. 따라서 본 논문은 이를 극복하여 실시간 비디오 피드를 기반으로 얼굴 인식 기술을 활용할 수 있는 실감형 인터렉티브 콘텐츠를 구현하고자 한다. 고해상도의 그래픽을 위해 Unity 엔진을 사용하여 제작되었고 그 과정에서 얼굴인식 성능 저하와 프레임 드랍(Frame Drop) 현상이 발생하여 추가적으로 Dlib 툴킷을 사용하고, 얼굴인식 이미지의 해상도를 조절함으로 해당 문제를 해결했다.

Keywords

References

  1. Byung-Hwa Park and Se-Young Oh, "Influence of Facial Shape and Texture on Human Face Recognition", The HCI Society of Korea, 2017.2, pp.78 - 81, 2017.
  2. Lee Young Ju and Junggyo Lee, "A Study on the Visual-Perceptive Seqence by the Color Contrast in Space Design", Journal of Korea Intitute of Spatial Design, vol.5, no.2, pp91-101, 2010. https://doi.org/10.35216/kisd.2010.5.2.91
  3. Park Beom Geun, "Biometric Technology and Market Trends", S&T Market Report Vol.39, pp.1-28, 2016.
  4. Kyoung Yul Bae, "Histogram Analysis for Performance Improvement of Face Recognition", J. Computer Software & Media Tech, 2003, Vol. 2. 200, pp.1-14, 2003.
  5. Moon Hyun Joon, "Face Recognition: A Survey", HCI 2008, 2008.2, pp.1683-1688, 2008.
  6. Shim, Yun Sun, "Constructing Metaphoric Media Space through Biometric Technology Convergence", The Korean Society of Science & Art 38, 2020.3, pp.219-229, 2020. https://doi.org/10.17548/ksaf.2020.03.30.219
  7. Byung-Joon Park, Wan-Tae Kim and Hyun-Sik Kim, "A study on face area detection using face features", Korea Information Electron Communication Technology, 2020.6, pp.206-211, 2020.
  8. Hwang Won-joon, Kim Ji-hwan, Han Dong-yoon and Kim Joon-mo, "A Survey of Face Recognition Technique for Real Media Processing", Journal of Broadcast Engineering, Vol.19, No.3, pp.111-122, 2014.
  9. Viola and Jones, "Rapid Object Detection using a Boosted Cascade of Simple Features", IEEE Computer Vision and Pattern Recognition, CVPR 2001, pp.1-8, 2001.
  10. A. Zelinsky, "Learning OpenCV---Computer Vision with the OpenCV Library (Bradski, G.R. et al.; 2008)[On the Shelf]", in IEEE Robotics & Automation Magazine, vol. 16, no. 3, pp.100-100, 2009. https://doi.org/10.1109/MRA.2009.933612
  11. Davis E. King, "Dlib-ml: A Machine Learning Toolkit", Journal of Machine Learning Research 10, 2009, pp.1755-1758, 2009.
  12. N. Dalal and B. Triggs, "Histograms of oriented gradients for human detection", 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR'05, pp.886-893, 2005.