Abstract
The mainstream algorithm model structure based on deep neural networks is relatively simple, and it is easy to lose effective information in the pooling layer, the recognition accuracy is not high, and the recognition speed is slow. To solve this problem, a fusion convolutional attention mechanism and AlexNet’s Finger language recognition algorithm (ATTAlexNet). By introducing the convolutional attention mechanism in the AlexNet network, it can effectively perform feature learning, realize feature screening, enhance the characterization ability of the network, and introduce the AdderNet to replace the multiplication operation of the convolution layer in the AlexNet network, enhance the robustness of the network, and improve the network calculation speed. The experimental results show that ATTAlexNet is superior to other comparison algorithms, and under the same experimental conditions, the recognition rate of ATTAlexNet is increased by 2.0%, which proves that the ATTAlexNet algorithm can effectively realize finger language recognition, has fast calculation speed, and good robustness.
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