• 中国计算机学会会刊
  • 中国科技核心期刊
  • 中文核心期刊

J4 ›› 2012, Vol. 34 ›› Issue (2): 116-122.

• 论文 • 上一篇    下一篇

基于Hermite神经网络的动态手势学习和识别

李文生1,解 梅1,2,邓春健1,姚 琼1   

  1. (1.电子科技大学中山学院计算机工程系,广东 中山 528402;2.电子科技大学电子工程学院,四川 成都 610054)
  • 收稿日期:2010-02-18 修回日期:2011-04-28 出版日期:2012-02-25 发布日期:2012-02-25

Dynamic Gesture Learning and Recognition Based on the Hermite Neural Network

LI Wensheng1,XIE Mei1,2,DENG Chunjian1,YAO Qiong1   

  1. (1.Department of Computer Engineering,Zhongshan Institute,University of Electronics Science and Technology of China,Zhongshan 528402;2.School of Electronic Engineering,University of Electronics Science and Technology of China,Chengdu 610054,China)
  • Received:2010-02-18 Revised:2011-04-28 Online:2012-02-25 Published:2012-02-25

摘要:

为提高动态手势学习速度和识别准确率,本文提出一种基于Hermite正交基前向神经网络的动态手势识别方法。利用Camshift算法实时跟踪手势运动轨迹,提取手势特征向量作为神经网络的输入;以Hermite正交基函数作为隐含层激励函数构造三层前向神经网络,并给出一种基于伪逆的直接计算权值方法和根据网络目标精度要求自适应确定隐含节点数目方法;运用训练好的Hermite神经网络识别动态手势。测试结果表明:Hermite神经网络能够提高网络的学习训练速度和精度,提高手势学习速度和识别准确率,而且在手势识别方面具有较好的鲁棒性和泛化能力。

关键词: Hermite神经网络;权值直接确定;隐含结点数自适应确定;指尖跟踪;动态手势识别

Abstract:

In order to improve the training speed and identification accuracy, a method of dynamic gesture recognition based on the Hermite orthogonal basis feedforward neural network is  put forward. At first, the CamShift algorithm is used to track trajectories of moving fingertips and the characteristic vector of gesture is extracted as the input of the neural network. Then, a feedforward neural network which hides the layer neurons is activated by a group of Hermite orthogonal polynomial functions is, and a method to determine the network weights directly and determine the number of hidden layer nodes adaptively is proposed. Finally, gestures based on machine vision are  recognized through the trained Hermite neural network. The experimental results show that the Hermite neural network can enhance the speed and precision of network training, improve the learning speed and identification accuracy of gesture recognition and has good robustness and generalization ability.

Key words: Hermite neural network;weights&rsquo, direct determination;hidden node number adaptive determination;fingertips tracking;dynamic gesture recognition