<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>With the continuous development and progress of the times, the ways of human-computer interaction have become more and more diverse. In order to reduce the spread of the new crown virus, gesture recognition has become a hot topic in the field of human-computer interaction in recent years. Traditional gesture recognition is affected by the environment and database, etc., with poor robustness and low accuracy. In order to improve the recognition rate of static gestures, this paper proposes to establish a deep learning model using CNN convolutional neural network, and a static gesture recognition method based on template matching. By establishing a palm template diagram, the gesture image to be recognized is matched with the template diagram based on the feature point, and the image is rotated after matching, and the template based on the grayscale value is matched again, so as to extract the gesture part. Through experimental proof, the algorithm can effectively improve the gesture recognition rate, the recognition accuracy rate reached 93.17%, and the recognition speed is faster.</p>
</abstract>

With the continuous development and progress of the times, the ways of human-computer interaction have become more and more diverse. In order to reduce the spread of the new crown virus, gesture recognition has become a hot topic in the field of human-computer interaction in recent years. Traditional gesture recognition is affected by the environment and database, etc., with poor robustness and low accuracy. In order to improve the recognition rate of static gestures, this paper proposes to establish a deep learning model using CNN convolutional neural network, and a static gesture recognition method based on template matching. By establishing a palm template diagram, the gesture image to be recognized is matched with the template diagram based on the feature point, and the image is rotated after matching, and the template based on the grayscale value is matched again, so as to extract the gesture part. Through experimental proof, the algorithm can effectively improve the gesture recognition rate, the recognition accuracy rate reached 93.17%, and the recognition speed is faster.

Research on Static Gesture Recognition Based on Deep Learning

School of Computer Science and Engineering

International Journal of Advanced Network, Monitoring and Controls

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

{"article-title":"Research on Static Gesture Recognition Based on Deep Learning"}

With the continuous development and progress of the times, the ways of human-computer interaction have become more and more diverse. In order to reduce the...

Gesture category	Recognition rate
Digital gesture 1	95%
Digital gesture 2	99%
Digital gesture 3	96%
Digital gesture 4	83%
Digital gesture 5	86%
Digital gesture good	100%

Research on Static Gesture Recognition Based on Deep Learning

Published Online: May 26, 2023

Page range: 31 - 38

DOI: https://doi.org/10.2478/ijanmc-2022-0034

Keywords
Static Gesture Recognition, Template Matching, Deep Learning, Convolutional Neural Networks

© 2022 Min Zhang et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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Gesture recognition rate training effect

Research on Static Gesture Recognition Based on Deep Learning

Published Online: May 26, 2023

Page range: 31 - 38

DOI: https://doi.org/10.2478/ijanmc-2022-0034

KeywordsStatic Gesture Recognition, Template Matching, Deep Learning, Convolutional Neural Networks

© 2022 Min Zhang et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Figure 1.

Figure 2.

Figure 3.

Figure 4.

Figure 5.

Figure 6.

Figure 7.

Figure 8.

Figure 9.

Figure 10.

Gesture recognition rate training effect

Keywords
Static Gesture Recognition, Template Matching, Deep Learning, Convolutional Neural Networks