Abstract: Static gesture recognition has caught special attention for its great application value in man-machine interaction.At the same time,the accuracy of gesture recognition is affected by the complexity of gesture background and the diversity of gesture morphology in a certain extent.In order to improve the accuracy of gesture recognition,a method was proposed,which is based on convolutional neural network(CNN) and random forest(RF).Firstly,the image of the static gesture is segmented,then the feature extraction function of convolution network is used to extract feature vectors,and finally the random forest classifier is used to classify these feature vectors.On the one hand,the CNN has the ability of layered learning and is able to collect more representative information on the picture.On the other hand,random forest shows randomness for samples and feature selection,meanwhile,it can be avoided easily that the results of each decision tree is averaged over fitting problem.This paper verified by using the static gesture data set,and the experimental results show that the proposed method can effectively identify the static gestures and achieve an average recognition rate of 94.56%.The method proposed in this paper was further compared with principal component analysis(PCA) and partial binary(LBP).The experimental results show that the classification and recognition effect with feature extraction by CNN is better than PCA and LBP.The recognition rate is 2.44% higher than that of PCA-RF methodand 1.74% higher than that of LBP-RF method.Finally,the recognition rate of the proposed method reaches 97.9%,which is higher than the other two traditional feature extraction methods.

Key words: Convolutional neural network, Random forest, Recognition, Static gesture