Hand Gesture Recognition using Convolutional Neural Network

S. Shanmugam, Lakshmanan S A, P. Dhanasekaran, P. Mahalakshmi, A. Sharmila
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Abstract

The implicit message is usually brought to the spectators through activities involving various body parts like hands, face and arms. This is prominently known as Gesture and many such gestures are generally performed through hands in an involuntary manner. Smartness is to keep tabs on these hand gestures and derive purposeful details out of it. Convolutional neural networks (CNN) track these complex movements and help in extracting prime features. In this paper, training and testing were done consecutively with the aid of images to check the effectiveness of CNN and results are presented.
基于卷积神经网络的手势识别
隐含的信息通常是通过涉及手、脸和手臂等身体部位的活动传递给观众的。这就是众所周知的手势,许多这样的手势通常是通过手无意识地完成的。聪明是密切关注这些手势,并从中获得有目的的细节。卷积神经网络(CNN)跟踪这些复杂的运动,并帮助提取主要特征。本文借助图像连续进行训练和测试,验证了CNN的有效性,并给出了结果。
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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