Hand gestures recognitions play an important role in human-computer interaction. To facilitate the understanding of computer vision-based hand gesture recognition, this paper describes a system for human-computer interaction through images' local features SURF, and we use threshold segmentation and bag-of-words algorithms to reduce the feature space dimensions. Leap motion is capable of collecting 800 images of hand gestures as 8 types efficiently. On the self-built database, we carry out experiments about SURF, LBP and geometric structure features by using SVM, RBF neural network and BP neural network to test performance and improve accuracy. The results of experiments indicate a good effect in aspect of recognition correctly of 99.5% using RBF neural network.