融合密集卷积与空间转换网络的手势识别方法

doi:10.11999/JEIT170627

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Abstract As an important milestone for the development of the artificial intelligence, gesture recognition enables the human-computer interaction and has received significantly growing research interest nowadays. However, the current technology for the gesture recognition has the low quality in the gesture rotation, translation and scaling. To solve the problem, a novel network structure named Densenet_V2 is proposed, and it is based on Dense Convolutional Networks (Densenet) and Spatial Transformer Networks (STN). Firstly, the input samples and feature maps are spatially transformed and aligned with the STN. Then the effective features of gestures are automatically extracted by using the Densenet. Finally, the linear classifier is adopted to classify the gestures. To prevent the network model from over-fitting the sample data set, the L2 regular term is involved into the loss function to achieve the weight decay when training the network. Experiments on the Marcel gesture database show that Densenet_V2 can improve the recognition rate of static deformation gestures.

Key words： Gesture recognition Deformation Dense convolutional networks Spatial Transformer Networks (STN) L2 regular term

Received: 29 June 2017 Published: 23 January 2018

PACS:

TP391.41

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

Corresponding Authors: MA Jie E-mail: jma@hebut.edu.cn

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	MA Jie
	ZHANG Xiudan
	YANG Nan
	TIAN Yalei

Cite this article:

MA Jie,ZHANG Xiudan,YANG Nan等. Gesture Recognition Method Combining Dense Convolutional with Spatial Transformer Networks[J]. JEIT, 2018, 40(4): 951-956.

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http://jeit.ie.ac.cn/EN/10.11999/JEIT170627 OR http://jeit.ie.ac.cn/EN/Y2018/V40/I4/951

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