摘要
人脸表情识别是模式识别领域的一个非常重要却十分复杂的研究课题。为了提高识别率和可靠性,提出了一种基于多层激励函数的量子神经网络和多级分类器组合的人脸表情识别方法。采用CMU人脸表情库进行训练和测试。实验结果表明,该识别方法在识别率和可靠性方面均有很好的效果,同时也体现了量子神经网络用于模式识别的优越性和潜力。
The facial expression recognition is an important and complicated problem of pattern recognition field. An approach to facial expression recognition based on multi-level transfer function quantum neural networks（ QNN）and multi-layer classifiers is presented in order to improve the recognition rate and recognition reliability. The QNN is trained and tested by the CMU face expression image database. The experiment results show that the method achieves excellent performance in terms of recognition rates and recognition reliadility, and the superiority and potential of QNN in solving pattern recognition problems are abvious.
出处
《控制工程》
CSCD
2008年第5期549-551,555,共4页
Control Engineering of China
基金
国家自然科学基金资助项目(60674092)
江苏省高科技研究基金资助项目(BG2006010)
关键词
量子神经网络
多层激励函数
多级分类器
人脸表情识别
quantum neural network
multi-level transfer function
multi-layer classifier
facial expression recognition
