Journal of Information Processing Systems

Korea Information Processing Society (한국정보처리학회)
권호 표지
DOI QR코드

DOI QR Code

A Vision-Based Method to Find Fingertips in a Closed Hand

  • Chaudhary, Ankit (Computer Vision Research Group, BITS Pilani) ;
  • Vatwani, Kapil (Computer Vision Research Group, BITS Pilani) ;
  • Agrawal, Tushar (Computer Vision Research Group, BITS Pilani) ;
  • Raheja, J.L. (Machine Vision Lab, CEERI/CSIR)
  • Received : 2011.10.18
  • Accepted : 2012.05.03
  • Published : 2012.09.30
Download PDF
⟨ Previous Next ⟩

Abstract

Hand gesture recognition is an important area of research in the field of Human Computer Interaction (HCI). The geometric attributes of the hand play an important role in hand shape reconstruction and gesture recognition. That said, fingertips are one of the important attributes for the detection of hand gestures and can provide valuable information from hand images. Many methods are available in scientific literature for fingertips detection with an open hand but very poor results are available for fingertips detection when the hand is closed. This paper presents a new method for the detection of fingertips in a closed hand using the corner detection method and an advanced edge detection algorithm. It is important to note that the skin color segmentation methodology did not work for fingertips detection in a closed hand. Thus the proposed method applied Gabor filter techniques for the detection of edges and then applied the corner detection algorithm for the detection of fingertips through the edges. To check the accuracy of the method, this method was tested on a vast number of images taken with a webcam. The method resulted in a higher accuracy rate of detections from the images. The method was further implemented on video for testing its validity on real time image capturing. These closed hand fingertips detection would help in controlling an electro-mechanical robotic hand via hand gesture in a natural way.

Keywords

References

  1. Chaudhary, A., Raheja, J.L., Singal, K., Raheja,S., An ANN based Approach to Calculate Robotic fingers positions, Published as Book Chapter in Lecture Notes in Computer Sciences, CCIS, Springer Berlin Heidelberg, Vol.192, No.3, 2011, pp.488-496.
  2. Chaudhary, A., Raheja, J. L., Das, K., Raheja, S., A Vision based Geometrical Method to find Fingers Positions in Real Time Hand Gesture Recognition, Journal of Software, Academy Publisher, Vol.7, 2012.
  3. Gastaldi G. and et al., "a man-machine communication system based on the visual analysis of dynamic gestures", International conference on image processing, Genoa, Italy, September, 2005, pp.397-400,.
  4. Kim, J. M., Lee, W. K., "Hand shape Recognition using fingertips", In the proceedings of fifth International Conference on Fuzzy Systems and Knowledge Discovery, Jinan, Shandong, China, October, 2008, pp.44-48.
  5. Lee, B., Chun, J., "Manipulation of virtual objects in marker-less AR system by fingertip tracking and hand gesture recognition", Proceedings of 2nd international conference on interaction science: Information Technology, Culture and Human, Seoul, Korea, 2009, pp.1110-1115.
  6. Lee, D., Park, Y., "Vision-Based Remote Control System by Motion Detection and Open Finger Counting", IEEE Transactions on Consumer Electronics, Vol.55, Issue 4, November, 2009, pp.2308- 2313. https://doi.org/10.1109/TCE.2009.5373803
  7. Nguyen, D.D., Pham, T.C., Jeon, J.W., "Fingertip Detection with Morphology and Geometric Calculation", IEEE/RSJ International Conference on Intelligent Robots and Systems, St. Louis, USA, October, 2009, pp.1460-1465.
  8. Nolker, C., Ritter, H., "Visual Recognition of Continuous Hand Postures", IEEE Transactions on neural networks, Vol.13, No.4, July, 2002, pp.983-994. https://doi.org/10.1109/TNN.2002.1021898
  9. Raheja, J.L., Chaudhary, "A., and Singal, K., Tracking of Fingertips and Centers of Palm Using KINECT", Proceedings of 3rd Internatinal Conference on Computational Intelligence, Modelling and Simulation (CIMSiM), 2011, 20-22, September, 2011, pp.248-252.
  10. Sawah, A.E. and et al., "A framework for 3D hand tracking and gesture recognition using elements of genetic programming", Proceedings of 4th Canadian conference on Computer and robot vision, Montreal, Canada, May, 2007, pp.495-502.
  11. Shin, M. C., Tsap, L. V. and Goldgof, D. B., "Gesture recognition using bezier curves for visualization navigation from registered 3d data", Pattern Recognition, Vol.37, Issue 5, May, 2004, pp.1011-1024. https://doi.org/10.1016/j.patcog.2003.11.007
  12. Zhou, H., Ruan, Q., A "Real-time Gesture Recognition Algorithm on Video Surveillance", Proceedings of 8th international conference on Signal Processing, Beijing , China, Vol.3, November, 2006.
  13. Raheja, J.L., Das, K., Chaudhary, "A. An Efficient Real time method of fingertip detection", Proceeding of 7th International Conference on Trends in Industrial Measurements and Automation (TIMA 2011), Chennai, INDIA, January, 2011, pp.447-450.
  14. Malik, S. Real-time Hand Tracking and Finger Tracking for Interaction, CSC2503F Project Report.
  15. Raheja J.L., Shyam R,. Kumar U., Prasad P.B., "Real-Time Robotic Hand Control using Hand Gesture", 2nd international conference on Machine Learning and Computing, 9-11 February, 2010, Bangalore, India, pp.12-16.
  16. Chaudhary, A., Raheja, J.L., Das, K., and Raheja, S., A Survey on Hand Gesture Recognition in context of Soft Computing,Published as Book Chapter in Advanced Computing CCIS, Springer Berlin Heidelberg, Vol.133, 2010, pp.46-55.
  17. Choi, J., Ko N., Ko D., "Morphological Gesture Recognition Algorithm, Proceeding of IEEE region" 10th international conference on Electrical and Electronic Technology, Coimbra, Portugal, 19-22 August, 2001, pp.291-296.

Cited by

  1. Evaluation of the Image Backtrack-Based Fast Direct Mode Decision Algorithm vol.8, pp.4, 2012, https://doi.org/10.3745/JIPS.2012.8.4.685
  2. Real time fabric defect detection system on an embedded DSP platform vol.124, pp.21, 2013, https://doi.org/10.1016/j.ijleo.2013.03.038
  3. Visual stereo matching combined with intuitive transition of pixel values vol.75, pp.23, 2016, https://doi.org/10.1007/s11042-015-2962-1
  4. Estimating material properties of deformable objects by considering global object behavior in video streams vol.74, pp.10, 2015, https://doi.org/10.1007/s11042-014-1995-1
  5. Secure eHealth-Care Service on Self-Organizing Software Platform vol.2014, 2014, https://doi.org/10.1155/2014/350876
  6. Sleep Monitoring System Using Kinect Sensor vol.2015, 2015, https://doi.org/10.1155/2015/875371
  7. Development of Hand Gesture Based Electronic Key Using Microsoft Kinect vol.218, pp.2261-236X, 2018, https://doi.org/10.1051/matecconf/201821802014