对偶树复小波与空域信息的手势识别分类研究

Research on gesture recognition and classification of dual-tree complex wavelet and spatial information

为提高手势识别中特征获取的有效性,本文提出空域特征与对偶树复小波变换特征相结合的融合特征,主要包括水平位置、竖直位置、长宽比、矩形度、Hu矩7个分量,及11维空域特征与对偶树复小波变换的16维特征进行融合后得到的27维特征。针对分类器优化算法,提出进行训练样本优选的最优距离–支持向量机(BD-SVM)分类方法。最后的实验结果表明,对"1~9"手势进行测试,当采用径向基核函数时,平均识别精度最高,为90.33%,平均识别时间为0.026 s,说明所提出的方法能够较好地进行静态手势识别,具有较高的训练速度和辨识精度。

To improve the validity of features obtained in gesture recognition, in this paper, we propose a fusion featurethat combines spatial and dual-tree complex wavelet transform features. These features mainly include seven compon-ents （horizontal position, vertical position, aspect ratio, rectangular degree, Hu moments, etc.） and 27 dimensional fea-tures, comprising 11 dimensional spatial features and 16 dimensional dual-tree complex wavelet transform features. Weemploy the optimal distance support vector machine （BD-SVM） classification method to optimize training samples forthe classifier optimization algorithm. The experimental results show that, in a test of gestures “1~9” using the RBF ker-nel function, the highest average recognition accuracy is 90.33% and the average recognition time is 0.026 s. These res-ults reveal that the proposed method demonstrates excellent static gesture recognition, a high training speed, and accur-acy in identification.