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A hand gesture recognition algorithm based on DC-CNN
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DOI：10.1007/s11042-019-7193-4.png)
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Abstract
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In the process of hand gesture recognition, the diversity and complexity of gesture will greatly influence the recognition rate and reliability. In the task of hand gesture recognition, the traditional method based on manual feature extraction is time-consuming, and the recognition rate is low. In order to improve the recognition rate, a novel recognition algorithm based on double channel convolutional neural network (DC-CNN) is proposed. Firstly, the preprocessing, denoising and edge detection of original gesture images are performed to obtain the hand edge images. Secondly, the hand gesture images and the hand edge images are respectively selected as two input channels of the CNN. Each channel contains the same number of convolutional layers and the same parameters, but each has a separate weight. Finally, the feature fusion is performed at the full connection layer and the output result is classified by softmax classifier. Experiments on the Jochen Triesch Database (JTD) and the NAO Camera hand posture Database (NCD) show that the proposed algorithm has improved the rate of hand gesture recognition and has enhanced the generalization ability of the CNN.
Keywords:
CNN
Double channel
DC-CNN
Gesture recognition
Deep learning
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