Gender roles influence behavior in social interactions. Thus, real-time gender recognition is essential in Human-Robot Interaction (HRI) for providing timely gender information to improve the experience of HRI. Considering the HRI scenario, a 3D-human-body-shape-based gender recognition is investigated. The 3D information is obtained by processing the depth image from an RGB-D camera. In addition, a machine learning method based on a Support Vector Machine (SVM) was applied. The experimental results showed that our system could achieve real-time accurate gender recognition. It enriched the diversity of existing methods for HRI application.