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首页 > 过刊浏览>2021年第30卷第2期 >20-27. DOI:10.15888/j.cnki.csa.007748
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基于多特征加权融合的静态手势识别
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基金项目:

国家自然科学基金(51405448); 浙江理工大学博士科研启动项目(11122932611817); 浙江省大学生科技成果推广项目(14530031661961); 国家级大学生创新创业训练计划(201910338012); 浙江理工大学大学生科创项目(11120032662029, 11120132662028)


Static Hand Gesture Recognition Based on Multi-Feature Weighted Fusion
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    摘要:

    针对人工提取特征的单一性及卷积神经网络提取特征的遗漏性问题, 提出了一种基于多特征加权融合的静态手势识别方法. 首先, 提取分割后的手势图像的傅里叶和Hu矩等形状特征, 将两者融合作为手势图像的局部特征; 设计双通道卷积神经网络提取手势图像的深层次特征, 采用主成分分析方法对提取的特征进行降维; 然后, 将提取的局部特征和深层次特征进行加权融合作为手势识别的有效特征描述; 最后, 使用Softmax分类器进行手势图像分类. 实验结果验证了提出方法的有效性, 在手势图像数据集上的识别准确率达到了99%以上.

    Abstract:

    Static hand gesture recognition based on multi-feature weighted fusion is proposed to solve the problems of singularity and omission in convolutional neural network for feature extraction. Firstly, the Fourier and Hu moments of the segmented gesture image are extracted and fused as the local features. Besides, a dual-channel convolutional neural network is designed to extract the deep features of the gesture image, which are further treated by dimensionality reduction by principal component analysis. Secondly, the extracted local and deep features are weighted and fused as effective description for hand gesture recognition. Finally, gesture images are classified with Softmax classifier. Experimental results verify the proposed method, and the recognition accuracy reaches over 99% on the image dataset.

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陈影柔,田秋红,杨慧敏,梁庆龙,包嘉欣.基于多特征加权融合的静态手势识别.计算机系统应用,2021,30(2):20-27

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  • 收稿日期:2020-05-28
  • 最后修改日期:2020-06-19
  • 在线发布日期: 2021-01-29
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