Abstract: Since the traditional shallow learning networks rely too much on manual selection of gesture features,they cannot adapt to complex and varied natural scenes in real time.Based on the convolutional neural network architecture,this paper proposes an improved multi-scale deep network gesture recognition model,which makes it possible to overcome the drawbacks of ma-nual extraction features by using the convolutional layer to automatically learn gesture features.In this method,the adaptive multi-scale features are introduced to realize that convolution kernels with different sizes at the same convolutional layer to gene-rate different scale features,and achieves feature map fusion with different levels by cascading shallow and deep features.In addition,in order to enhance the generalization ability of the model,this paper proposes a loss function based on regularization constraints.The experimental results show that the recognition accuracy of the proposed network model is higher than that of the ordinary single -scale convolutional neural network,and the shortcomings of imprecise and incomprehensive extraction as well as poor stability are overcome,and the time required for network training is not greatly increased.

Key words: Convolution feature, Deep learning, Gesture recognition, Loss function, Multi-scale, Regularization