【摘要】 手势识别作为人机交互的重要手段,其识别率的提高是当今重要的研究方向。通过分析当今三维手势识别技术的发展状况,采用Leap Motion设备针对三维动态手势进行数据采集和处理,使用SVM与PNN模式识别算法,分别进行了两类手势区分以及多类手势区分的仿真实验。结果表明,经过优化的PNN对动态手势均较SVM能达到更高的识别率,且经过PCA处理后运算效率较高。更多还原

【Abstract】 Gesture recognition is an essential means of human-computer interaction. The improvement of recognition rate is an important research direction today. By analyzing the development of today ’ s 3 D gesture recognition technology,Leap Motion equipment is used for data acquisition and processing of 3 D dynamic gestures. Using SVM and PNN pattern recognition algorithms,two kinds of gesture differentiation and multi-class gesture differentiation simulation experiments are carried out. The results show that the optimized PNN can achieve higher recognition rate than dynamic SVM,and the computational efficiency is higher after PCA processing.更多还原