随着先进人机交互技术的提出及发展，手势识别正成为其中一项关键技术，基于视觉的手势识别是当前涉及图象处理，模式识别，计算机视觉等领域的一个比较活跃的课题，由于Hausdorff距离模板匹配的方法具有计算量小，适应性强的特点，因此基于Hausdorff距离，建立了一个手势识别系统，该系统采用边缘特征像素点作为识别特征，并首次利用Hausdorff距离模板匹配的思想，在距离变换空间内，实现了中国手指字母集上的基于单目视觉的30个手指字母的手势识别，为提高系统的鲁棒性，还提出了修正的Hausdorff距离形式，测试集上的平均识别率为96．7％，实验结果表明，基于Hausdorff距离的模板匹配方法用于基于听觉的静态手势识别是可行的。

With the development of the advanced techniques of human computer interaction(HCI), gesture recognition is becoming one of the key techniques of HCI. Due to some notable advantages of vision based gesture recognition(VGR), e.g. more naturalness to HCI, now VGR is an active research topic in the fields of image processing, pattern recognition, computer vision and others. The method of model matching using Hausdorff distance has the characters of low computing cost and strong adaptability. The system described in this paper applies the hausdorff distance for the first time to visually recognize the chinese finger alphabet(CFA) gestures(total 30 gestures) with the recognition features of edge pixels in the distance transform space. In order to improve the robust performance of the system, the modified hausdorff distance(MHD) has been proposed and applied in the recognition process. The average recognition rate of the system using MHD is up to 96 7% on the testing set. The experimental result of the system shows that using the method of model matching based on the Hausdorff distance to realize the vision based static gesture recognition is feasible.