Real-Time Hand Gesture Recognition

Authors

  • Pranjali Manmode  M-Tech Student, Computer Science & Engineering, RTMNU, Nagpur, Maharashtra, India
  • Rupali Saha  Assistant Professor, Computer Science & Engineering, RTMNU, Nagpur, Maharashtra, India
  • Manisha N. Amnerkar  Assistant Professor, Computer Science & Engineering, RTMNU, Nagpur, Maharashtra, India

DOI:

https://doi.org//10.32628/CSEIT2173132

Keywords:

Hand Gesture Segmentation, Hand Gesture Tracking, Hand Gesture Recognition, Neural Network

Abstract

With the rapid development of computer vision, the demand for interaction between humans and machines is becoming more and more extensive. Since hand gestures can express enriched information, hand gesture recognition is widely used in robot control, intelligent furniture, and other aspects. The paper realizes the segmentation of hand gestures by establishing the skin color model and AdaBoost classifier based on haar according to the particularity of skin color for hand gestures and the denaturation of hand gestures with one frame of video being cut for analysis. In this regard, the human hand is segmented from a complicated background. The camshaft algorithm also realizes real-time hand gesture tracking. Then, the area of hand gestures detected in real-time is recognized by a convolutional neural network to discover the recognition of 10 common digits. Experiments show 98.3% accuracy.

References

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IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology

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Published

2021-06-30

Issue

Volume 7, Issue 3, May-June-2021

Section

Research Articles

License

Copyright (c) IJSRCSEIT

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

[1]
Pranjali Manmode, Rupali Saha, Manisha N. Amnerkar, " Real-Time Hand Gesture Recognition, IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 7, Issue 3, pp.618-624, May-June-2021. Available at doi : https://doi.org/10.32628/CSEIT2173132