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类间学习神经网络的人脸表情识别周书仁 梁昔明 杨秋芬 刘国英


  摘 要:针对目前表情识别类间信息无关状态,提出了一种表情类间学习的神经网络分类识别算法。该算法首先构建一个BP网络学习对和一个距离判据单元,该距离判据单元仅用来计算类间的实际距离,类间期望距离是根据大量实验结果获得的;然后通过类内实际输出和类间期望距离来修正该网络;最后给出一组实例样本进行表情分类识别。实验结果表明,该算法能有效地识别人脸表情,能紧密地将各类表情间的信息联系起来,效率和准确性均有明显提高。

  关键词:表情识别;类间学习;神经网络;类间期望;距离判据

  中图分类号:TP391 文献标志码:A

   文章编号:1001-3695(2008)07-2219-04

  

  Facial expression recognition using neural network of congener learning

  ZHOU Shu-ren1,2 , LIANG Xi-ming1 , YANG Qiu-fen1 , LIU Guo-ying2

  

  (1. School of Information Science & Engineering, Central South University, Changsha410083, China; 2.School of Computer & Communication Engineering, Changsha University of Science & Technology, Changsha410076, China)

  

  Abstract: In view of the current unrelated status of expressions recognition in congener information, this paper proposed an algorithm of the neural network classification of expression congener learning. The algorithm first built a pair of network of BP and an unit of the distance judgment evidence which was only used to calculate the actual distance between categories, and the congener expected distance was obtained under a lot of experiments, then amended the network by the actual output of inner class and the expected distance of congener, finally tested expression recognition through a set of samples. The experimental results demonstrate the feasibility of the algorithm which is able to link information between two expressions at random closely. It shows that the algorithm can improve the efficiency and accuracy of facial expression recognition obviously.

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