为实现基于运动轨迹信息的动态手指姿势的识别，促进人机交互和虚拟现实交叉领域的发展,介绍了一种基于Leap Motion的三维(3D)空间内的指势关键特征点轨迹识别的方法。将Leap motion controller获取的模型化的手部信息经过手部骨架算法提取出手部骨架目标，然后，利用细化算法得到手指直线示意图，并提取指势关键特征点及运动轨迹。利用支持向量机(SVM)对3D基本动态指势进行识别和评估。实验结果表明，该方法能够在较为复杂的3D空间内，即使手部出现自遮挡的情况下，实现动态指势的识别，准确率较高，稳健性强。

In order to achieve the recognition of dynamic finger gestures based on motion trajectory and promote the development of the cross-cutting areas of human-computer interaction and virtual reality, a recognition method of the key feature points′trajectory is described, which is about finger gesture in three-dimensional (3D) space on basic of Leap Motion. The hand skeleton is extracted by skeleton algorithms with the modeling hand information, which is obtained by the Leap motion controller. Then, the thinning algorithm is used to get straight finger schematic, and extracts feature points and motion trajectory. The basic dynamic 3D finger gestures are identified and evaluated by means of support vector machine (SVM). Experimental results show that the identification of dynamic 3D finger gestures can be achieved in a more complex 3D space or hand block by themselves, and has a high accuracy and robustness.