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融合密集卷积与空间转换网络的手势识别方法

马杰 张绣丹 杨楠 田亚蕾

马杰, 张绣丹, 杨楠, 田亚蕾. 融合密集卷积与空间转换网络的手势识别方法[J]. 电子与信息学报, 2018, 40(4): 951-956. doi: 10.11999/JEIT170627
引用本文: 马杰, 张绣丹, 杨楠, 田亚蕾. 融合密集卷积与空间转换网络的手势识别方法[J]. 电子与信息学报, 2018, 40(4): 951-956. doi: 10.11999/JEIT170627
MA Jie, ZHANG Xiudan, YANG Nan, TIAN Yalei. Gesture Recognition Method Combining Dense Convolutional with Spatial Transformer Networks[J]. Journal of Electronics & Information Technology, 2018, 40(4): 951-956. doi: 10.11999/JEIT170627
Citation: MA Jie, ZHANG Xiudan, YANG Nan, TIAN Yalei. Gesture Recognition Method Combining Dense Convolutional with Spatial Transformer Networks[J]. Journal of Electronics & Information Technology, 2018, 40(4): 951-956. doi: 10.11999/JEIT170627

融合密集卷积与空间转换网络的手势识别方法

doi: 10.11999/JEIT170627
基金项目: 

国家自然科学基金(61203245),河北省自然科学基金(F2012202027)

Gesture Recognition Method Combining Dense Convolutional with Spatial Transformer Networks

Funds: 

The National Natural Science Foundation of China (61203245), The Natural Science Foundation of Hebei Province (F2012202027)

  • 摘要: 手势识别作为人机交互的方式之一,在人工智能日益发展的今天备受瞩目。针对手势旋转、平移、缩放等形变导致识别率偏低的问题,该文基于密集卷积网络(Densenet)与空间转换网络(STN)提出了一种新型的网络结构Densenet_V2,先利用空间转换网络对输入的样本和特征图进行空间变换和对齐,再利用密集卷积网络自动提取手势的有效特征,最后通过线性分类器对手势进行分类。为防止网络模型对样本数据集过度拟合,对网络进行训练时在损失函数中加入L2正则项以实现权重衰减。在Marcel手势库上进行多次实验。实验结果表明,Densenet_V2可以提高对静态形变手势的识别率。
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出版历程
  • 收稿日期:  2017-06-29
  • 修回日期:  2017-11-28
  • 刊出日期:  2018-04-19

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