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Depth Camera Based Real-Time Fingertip Detection Using Multi-view Projection

  • Weixin Yang
  • Zhengyang Zhong
  • Xin Zhang
  • Lianwen Jin
  • Chenlin Xiong
  • Pengwei Wang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8008)

Abstract

We propose a real-time fingertip detection algorithm based on depth information. It can robustly detect single fingertip regardless of the position and direction of the hand. With the depth information of front view, depth map of top view and side view is generated. Due to the difference between finger thickness and fist thickness, we use thickness histogram to segment the finger from the fist. Among finger points, the farthest point from palm center is the detected fingertip. We collected over 3,000 frames writing-in-the-air sequences to test our algorithm. From our experiments, the proposed algorithm can detect the fingertip with robustness and accuracy.

Keywords

Kinect depth image finger detection fingertip detection multiview projection 

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References

  1. 1.
    Feng, Z., Xu, S., Zhang, X., Jin, L., Ye, Z., Yang, W.: Real-time fingertip tracking and detection using Kinect depth sensor for a new writing-in-the air system. In: International Conference on Internet Multimedia Computing and Service (ICIMCS 2012), pp. 70–74 (2012)Google Scholar
  2. 2.
    Malik, S., Laszlo, J.: Visual touchpad: a two-handed gestural input device. In: Proceedings of international Conference on Multimodal Interfaces, pp. 289–296. ACM (2004)Google Scholar
  3. 3.
    Jin, L., Yang, D., Zhen, L., Huang, J.: A novel vision based finger-writing character recognition system. Journal of Circuits, Systems, and Computers (JCSC) 16(3), 421–436 (2007)CrossRefGoogle Scholar
  4. 4.
    Crowley, J., Berard, F., Coutaz, J.: Finger tracking as an input device for augmented reality. In: International Workshop on Gesture and Face Recognition, pp. 195–200 (1995)Google Scholar
  5. 5.
    Minnen, D., Zafrulla, Z.: Towards robust cross-user hand tracking and shape recognition. In: IEEE International Conference on Computer Vision Workshops, pp. 1235–1241 (2011)Google Scholar
  6. 6.
    Pugeault, N., Bowden, R.: Spelling it out: Real-time asl fingerspelling recognition. In: IEEE International Conference on Computer Vision Workshops, pp. 1114–1119 (2011)Google Scholar
  7. 7.
    Shotton, J., Fitzgibbon, A., Cook, M., Sharp, T., Finocchio, M., Moore, R., Kipman, A., Blake, A.: Real-time human pose recognition in parts from single depth images. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), vol. 2, p. 7 (2011)Google Scholar
  8. 8.
    Tang, Y., Sun, Z., Tan, T.: Real-time head pose estimation using random regression forests. In: Sun, Z., Lai, J., Chen, X., Tan, T. (eds.) CCBR 2011. LNCS, vol. 7098, pp. 66–73. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  9. 9.
    Wu, A., Shah, M., Da Vitoria Lobo, N.: A virtual 3D blackboard: 3D finger tracking using a single camera. In: International Conference on Automatic Face and Gesture Recognition, pp. 536–543 (2000)Google Scholar
  10. 10.
    Kang, S., Nam, M., Rhee, P.: Color based hand and finger detection technology for user interaction. In: International Conference on Convergence and Hybrid Information Technology, pp. 229–236 (2008)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Weixin Yang
    • 1
  • Zhengyang Zhong
    • 1
  • Xin Zhang
    • 1
  • Lianwen Jin
    • 1
  • Chenlin Xiong
    • 1
  • Pengwei Wang
    • 1
  1. 1.School of Electronic and Information EngineeringSouth China University of TechnologyGuangzhouP.R. China

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