A simple and effective static gesture recognition method based on attention mechanism

To solve the problem of low sign language recognition rate under the condition of small samples, a simple and effective static gesture recognition method based on an attention mechanism is proposed. The method proposed in this paper can enhance the features of both the details and the subject of the gesture image. The input of the proposed method depends on the intermediate feature map generated by the original network. Also, the proposed convolutional model is a lightweight general module, which can be seamlessly integrated into any CNN(Convolutional Neural Network) architecture and achieve significant performance gains with minimal overhead. Experiments on two different datasets show that the proposed method is effective and can improve the accuracy of sign language recognition of the benchmark model, making its performance better than the existing methods.