[1]杨红玲,宣士斌,莫愿斌.基于卷积神经网络的手势识别[J].计算机技术与发展,2018,28(07):11-14.[doi:10.3969/ j. issn.1673-629X.2018.07.003]
　YANG Hong-ling,XUAN Shi-bin,MO Yuan-bin.Hand Gesture Recognition Based on Convolutional Neural Network[J].,2018,28(07):11-14.[doi:10.3969/ j. issn.1673-629X.2018.07.003]
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基于卷积神经网络的手势识别()
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《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
28
期数:
2018年07期
页码:
11-14
栏目:
智能、算法、系统工程
出版日期:
2018-07-10

文章信息/Info

Title:
Hand Gesture Recognition Based on Convolutional Neural Network
文章编号:
1673-629X(2018)07-0011-04
作者:
杨红玲; 宣士斌; 莫愿斌
广西民族大学 信息科学与工程学院,广西 南宁 530006
Author(s):
YANG Hong-ling; XUAN Shi-bin; MO Yuan-bin
School of Information Science and Engineering,Guangxi University for Nationalities,Nanning 530006,China
关键词:
机器学习; 卷积神经网络; 手势识别; 准确率
Keywords:
machine learning; convolutional neural networks; gesture recognition; accuracy
分类号:
TP301
DOI:
10.3969/ j. issn.1673-629X.2018.07.003
文献标志码:
A
摘要:
在手势识别的过程中,手势变化的多样性和手势本身的复杂性会对手势识别的精确性和可靠性带来更大的影响。为了能够在实现高准确率手势识别的同时降低识别速度,提出了一种基于深度卷积神经网络的准确的手势识别方法。 该方法首先运用边缘检测算法和细化算法提取手势区域的边缘轮廓特征和手势骨架特征,然后采用特征融合的方法获取手势融合特征,最后通过对比几种常见机器学习算法(支持向量机、决策树、随机森林和卷积神经网络)在手势识别中的时间效率和准确精度,选取最优的手势识别模型。 实验结果表明,在不同数据集下,通过实验数据对比,基于深度神经网络的手势识别虽然在平均时间开销上相对较高,但在识别准确率上却提升了 2%,可以达到 98.57%。
Abstract:
In the process of hand gesture recognition,the diversity of gesture changes and the complexity of gesture itself have greater impact on the accuracy and reliability of hand gesture recognition. In order to reduce hand gesture recognition speed while achieving high accuracy of gesture recognition,we propose an accurate gesture recognition method based on deep convolution neural network. Firstly,the edge detection algorithm and the refinement algorithm are used to extract the edge contour feature and gesture skeleton features of the gesture region. Then the feature fusion method is used to obtain the gesture fusion feature. Finally by comparing several common machine learning algorithms like support vector machines,decision tree,random forests and convolutional neural networks on the time efficiency and accuracy of gesture recognition,the optimal gesture recognition model is selected. The experiment shows that under the different data sets,the average time cost of the gesture recognition based on the deep neural network is higher than other algorithms,but the rate of recognition accuracy is improved by 2% and can reach 98.57%.

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更新日期/Last Update: 2018-08-23