Journal of the Korea Society of Computer and Information (한국컴퓨터정보학회논문지)

Korean Society of Computer Information (한국컴퓨터정보학회)
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Hand Feature Extraction Algorithm Using Curvature Analysis For Recognition of Various Hand Gestures

다양한 손 제스처 인식을 위한 곡률 분석 기반의 손 특징 추출 알고리즘

  • 윤홍찬 (가천대학교 일반대학원 IT융합공학과) ;
  • 조진수 (가천대학교 IT대학 컴퓨터공학과)
  • Received : 2015.03.04
  • Accepted : 2015.04.10
  • Published : 2015.05.30
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Abstract

In this paper, we propose an algorithm that can recognize not only the number of stretched fingers but also determination of attached fingers for extracting features required for hand gesture recognition. The proposed algorithm detects the hand area in the input image by the skin color range filter based on a color model and labeling, and then recognizes various hand gestures by extracting the number of stretched fingers and determination of attached fingers using curvature information extracted from outlines and feature points. Experiment results show that the recognition rate and the frame rate are similar to those of the conventional algorithm, but the number of gesture cases that can be defined by the extracted characteristics is about four times higher than the conventional algorithm, so that the proposed algorithm can recognize more various gestures.

본 논문에서는 손 제스처 인식에 필요한 특징 추출을 위하여 손가락의 개수뿐만 아니라 붙어있는 손가락 판별까지 인식할 수 있는 알고리즘을 제안한다. 제안하는 알고리즘은 컬러모델 기반의 피부색 범위 필터와 레이블링을 통하여 입력 영상에서 손 영역을 검출하고, 외곽선 및 특징점과 이들로부터 추출한 곡률 정보를 이용해 펴진 손가락의 개수 및 붙어있는 손가락 판별을 통한 특징을 추출하여 다양한 손 제스쳐를 인식한다. 실험결과 인식률과 처리 가능 프레임 레이트(frame rate)는 기존 알고리즘과 유사하였지만, 추출된 특징을 가지고 정의할 수 있는 제스처의 경우의 수는 기존 알고리즘보다 약 4배 정도 많아 훨씬 더 다양한 제스처를 인식할 수 있음을 알 수 있었다.

Keywords

References

  1. S. Mitra and T. Acharya, "Gesture Recognition: A Survey," IEEE Trans. on Systems, Man, and Cybernetics, vol.37, no.3, pp.311-324, May 2007 https://doi.org/10.1109/TSMCC.2007.893280
  2. M. Y Na, H. J You and T. Y Kim, "A Vision-based Real-time Hand Pose and Gesture Recognition Method for Smart Device Control", THE JOURNAL OF KOREAN INSTITUTE OF NEXT GENERATION COMPUTING, Vol. 8, No. 4, pp. 27-34, August 2012
  3. H. D Seo, H. R Kim and Y. H Joo, "Feature Extraction of Hand Region Using Center of Gravity", Proceedings of KIIS Fall Conference, Vol. 21, No. 2, pp. 163-164, Korea, November 2011
  4. H. S Jeong and Y. H Joo, "Feature Point Extraction of Hand Region Using Vision", The Transactions of the Korean Institute of Electrical Engineers, Vol. 58, No. 10, pp.2041-2046, October 2009
  5. I. K Choi and J. S Yoo "Hand shape recognition based on geometric feature using the convex-hull", Journal of the Korea Institute of Information and Communication Engineering, Vol. 18, No. 8, pp. 1931-1940, August 2014 https://doi.org/10.6109/jkiice.2014.18.8.1931
  6. J. H Lee, J. M Kim, S. W Cho, "Information Fusion for Object Detection and Tracking", Proceedings of the IEEK Conference II, pp. 707-708, Korea, November 2007.
  7. M. C Kim and J. H Oh, "Skin Tone Enhancement Based on Human Favorite Skin Color", Proceedings of the IEEK Conference, Vol. 25 No.1, pp. 5-8, Korea, June 2002.
  8. B. R Lee, G. Y Kim, K. K Park and B. E Min, "Comparative Study on Connected Component Labeling",IEIE WorkShop Vol. 9, pp. 86-91, 1997
  9. M. Kass, A. Witkin, D. Terzopoulos, "Snakes: Active contour models", International Journal of Computer Vision, Vol. 1, pp. 321-331, January 1988. https://doi.org/10.1007/BF00133570
  10. D. Douglas, T. Peucker, "Algorithms for the reduction of the number of points required to represent a digitized line or it caricature", Canada. Cartographer, Vol. 10, pp. 111-122, 1973.
  11. S. G. Aki and G. T. Toussaint, "Efficient convex hull algorithms for pattern recognition applications", Int'l Joint Conf. on Pattern Recognition, pp. 483-487, 1978.
  12. G. Borgefors, "Distance transformations in digital images," Computer Vision, Graphics and Image Processing, Vol. 34, pp. 344-371, 1986. https://doi.org/10.1016/S0734-189X(86)80047-0