As robots have become more pervasive in our daily life, natural human-robot interaction (HRI) has had a positive impact on the development of robotics. Thus, there has been growing interest in the development of vision-based hand gesture recognition for HRI to bridge human-robot barriers. The aim is for interaction with robots to be as natural as that between individuals. Accordingly, incorporating hand gestures in HRI is a significant research area. Hand gestures can provide natural, intuitive, and creative methods for communicating with robots. This paper provides an analysis of hand gesture recognition using both monocular cameras and RGB-D cameras for this purpose. Specifically, the main process of visual gesture recognition includes data acquisition, hand gesture detection and segmentation, feature extraction and gesture classification, which are discussed in this paper. Experimental evaluations are also reviewed. Furthermore, algorithms of hand gesture recognition for human-robot interaction are examined in this study. In addition, the advances required for improvement in the present hand gesture recognition systems, which can be applied for effective and efficient human-robot interaction, are discussed.

Hand posture recognition technology makes human–computer interaction more natural and efficient. Existing hand posture recognition algorithms are mainly based on red, green, blue (RGB) images or depth data, each of which has their limitations: the former are susceptible to the interference of lighting and background color, while, for the latter, it is difficult to capture details, which affects the accuracy. To overcome these problems, the fusion of RGB images and depth data has become a research trend. However, traditional static fusion methods use fixed modal weights, which are difficult to adapt to the complex relationships between modalities and lead to performance degradation.