关键词: 手势识别, 高斯肤色模型, 深度学习, 卷积神经网络

Abstract: During the research process of gesture recognition, it is difficult for the manual selection features to adapt to the variability of gestures. In view of this, this paper proposes a new method of gesture recognition, which combines color models and convolution neural network. In terms of the collected gesture image in different circumstances, firstly, it uses Gaussian skin color model to segment the gesture area. Then it takes advantage of convolution neural network to build a gesture recognition model which combines the process of the extraction with that of classification of gesture feature and simulates visual transduction and cognition, effectively avoiding the subjectivity and limitation of the manual features selection. Gesture recognition model regards the gray information in gesture area as input, and simultaneously takes advantage of the weight sharing and pooling techniques and so on to decrease the number of network weight value and lower the complexity of the model. The results of the experiment show that via the method of Convolution Neural Network（CNN）, the feature learning can be realized effectively, and the average rate of gesture recognition can reach 95% under different data sets. By comparing with the traditional methods, it shows that the method in this paper owns higher recognition rate and real-time feature.

Key words: gesture recognition, Gaussian skin color model, deep learning, convolution neural network