提出并实现一个用于人机交互的静态手势识别系统.基于皮肤颜色模型进行手势分割,并用傅里叶描述子描述轮廓.采用针对小样本特别有效且范化误差有界的支持向量机方法:最小二乘支持向量机(LS-SVM)作为分类器.提出了LS-SVM的增量训练方式,避免了费时的矩阵求逆操作.为实现多类手势识别,利用DAG(Directed Acyclic Graph)将多个两类LS-SVM结合起来.对26个字母手势进行识别,与多层感知器、径向基函数网络等方法比较,LS-SVM的识别率最高,为93.62%.

A static hand gesture recognition system for human-robot interaction is proposed and realized. Hand area is segmented based on skin color model, then the hand contour is described by use of Fourier descriptor. LS-SVM is used as classifier, which is effective to small sample and has limited generalized error. A new incremental training algorithm for LS-SVM is proposed, which avoids the time-cost computation for matrix inverse. DAGSVM algorithm is used to combine two-class LS-SVM to realize multi-class gesture recognition. The recognition rate of 26 alphabetical gestures is the highest (93.62%) comparing with multi-layer perception, radial basis function network etc.