Gesture recognition based on multilevel multimodal feature fusion

With the development of human-computer interaction, gesture recognition has gradually become one of the research hotspots. The cost reduction and the richer information of RGB-D images make the research of gesture recognition based on RGB-D images more and more. However, the current gesture processing methods for RGB-D images still can not fully utilize the information contained. Aiming at the above problems, this paper studies the feature extraction method of RGB-D image, and proposes a multimodal and multilevel feature extraction method. By extracting multimodal and multilevel image features for mapping and splicing, the utilization of RGB-D image information and the accuracy in recognition are improved effectively. Finally, the experiments verified the effectiveness and robustness of the proposed method based on the self-built gesture database. Compared and analyzed with several other RGB-D processing methods, the processing method of this paper is more advanced and effective, and can achieve better results in gesture recognition.