In this paper, we present an efficient method to incrementally learn to classify static hand gestures. This method allows users to teach a robot to recognize new symbols in an incremental manner. Contrary to other works which use special sensors or external devices such as color or data gloves, our proposed approach makes use of a single RGB camera to perform static hand gesture recognition from 2D images. Furthermore, our system is able to incrementally learn up to 38 new symbols using only 5 samples for each old class, achieving a final average accuracy of over 90\%. In addition to that, the incremental training time can be reduced to a 10\% of the time required when using all data available.

翻译：在本文中，我们提出了一种有效的方法，用于增量学习分类静态手势。该方法允许用户逐步教授机器人识别新的符号。与其他使用特殊传感器或外部设备（如彩色或数据手套）的作品相反，我们提出的方法利用单个RGB相机从2D图像中执行静态手势识别。此外，我们的系统能够仅使用每个旧类的5个样本，增量学习多达38个新符号，并实现最终平均精度超过90％。此外，增量训练时间可减少到使用所有可用数据所需时间的10％。