Hand Gesture Recognition Based on Hausdorff Distance
基于Hausdorff距离的手势识别

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.