关键词: 静态手势识别, 卷积神经网络, 双通道, 卷积核

Abstract: In static gesture recognition, the traditional methods based on the manual feature extraction are time consuming and labor intensive, while the recognition rates are low. The existing convolution neural networks with single convolution kernel are not sufficient to extract features. To solve the problem, this paper proposes a double channel convolutional neural network, provided by two channels with diverse convolution kernel sizes. The model is able to extract image features from multiple scales and combine them in the fully connected layer. The experiments on Thomas Moeslund and Jochen Triesch gesture databases show that the model improves the accuracy of static gestures and enhances the generalization ability of convolution neural networks.

Key words: static gesture recognition, convolution neural network, double channel, convolution kernel