Abstract
Hand gesture recognition is an important topic in human-computer interaction. However, most of the current methods are complicated and time-consuming, which limits the use of hand gesture recognition in real-time circumstances. In this paper, we propose a data fusion-based hand gesture recognition model by fusing depth information and skeleton data. Because of the accurate segmentation and tracking with Kinect V2, the model can achieve real-time performance, which is 18.7% faster than some of the state-of-the-art methods. Based on the experimental results, the proposed model is accurate and robust to rotation, flip, scale changes, lighting changes, cluttered background, and distortions. This ensures its use in different real-world human-computer interaction tasks.
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