Dynamic Hand Gesture Recognition

2022 IEEE 2nd International Symposium on Sustainable Energy, Signal Processing and Cyber Security (iSSSC) Pub Date : 2022-12-15 DOI:10.1109/iSSSC56467.2022.10051259

Subash Chandra Bose Jaganathan, K. R, Thevaprakash P, Krishna Basak, Shinjan Verma, Anisha Mital

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Abstract

Gestures were most likely utilised by our ancestors to communicate. Armstrong once stated that he believes movements using the hands were the earliest form of complex human communication. The beginning stage of human computer Interactions is a gesture recognition system. Here, we have designed a Dynamic Hand Gesture Recognition (HGR) System using a Neural Network, which can recognize the gesture using the Computer or Laptop’s Web Camera and do the corresponding tasks. The model has been divided into mainly three modules. Firstly, Module 1 is about building a CNN model which we use to predict the gestures. Our second module is about predicting the gesture through live video feed. The final module is about assigning a specified task for a particular predicted gesture. For the predicted gesture, we use PyAutoGUI module to control devices like mouse and keyboard. Finally, the system is made to do some desired tasks in response to the gestures and obtains 99.84% of accuracy.

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动态手势识别

手势最有可能被我们的祖先用来交流。阿姆斯特朗曾经说过，他相信用手的动作是人类复杂交流的最早形式。人机交互的起始阶段是手势识别系统。本文设计了一个基于神经网络的动态手势识别系统(HGR)，该系统可以通过计算机或笔记本电脑的网络摄像头识别手势并执行相应的任务。该模型主要分为三个模块。首先，模块1是关于建立一个CNN模型，我们用它来预测手势。我们的第二个模块是通过实时视频来预测手势。最后一个模块是关于为特定的预测手势分配指定任务。对于预测的手势，我们使用PyAutoGUI模块来控制鼠标和键盘等设备。最后，通过对手势的响应，使系统完成相应的任务，准确率达到99.84%。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

2022 IEEE 2nd International Symposium on Sustainable Energy, Signal Processing and Cyber Security (iSSSC)

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