In order to solve the problem that real-time face recognition is susceptible to illumination changes,this paper proposes a face recognition method that combines Local Binary Patterns(LBP)and Embedded Hidden Markov Mod...In order to solve the problem that real-time face recognition is susceptible to illumination changes,this paper proposes a face recognition method that combines Local Binary Patterns(LBP)and Embedded Hidden Markov Model(EHMM).Face recognition method.The method firstly performs LBP preprocessing on the input face image,then extracts the feature vector,and finally sends the extracted feature observation vector to the EHMM for training or recognition.Experiments on multiple face databases show that the proposed algorithm is robust to illumination and improves recognition rate.展开更多
针对传统的说话人分割聚类系统中,由于聚类时话者信息不足而影响切分准确度的问题,本文提出了一种基于进化隐马尔科夫模型和交叉对数似然比距离测度的多层次说话人分割聚类算法,在传统的话者分割聚类算法的基础上引入了重分割和重聚类...针对传统的说话人分割聚类系统中,由于聚类时话者信息不足而影响切分准确度的问题,本文提出了一种基于进化隐马尔科夫模型和交叉对数似然比距离测度的多层次说话人分割聚类算法,在传统的话者分割聚类算法的基础上引入了重分割和重聚类的机制,以及基于距离测度和贝叶斯信息准则的分层聚类算法,有效的解决了传统方法中切分准确度受到话者信息制约的问题.在美国国家标准技术署(NIST)2003 Spring RT数据库上的实验结果表明,本文提出的算法比传统算法系统性能相对提高了41%.展开更多
文摘In order to solve the problem that real-time face recognition is susceptible to illumination changes,this paper proposes a face recognition method that combines Local Binary Patterns(LBP)and Embedded Hidden Markov Model(EHMM).Face recognition method.The method firstly performs LBP preprocessing on the input face image,then extracts the feature vector,and finally sends the extracted feature observation vector to the EHMM for training or recognition.Experiments on multiple face databases show that the proposed algorithm is robust to illumination and improves recognition rate.
文摘针对传统的说话人分割聚类系统中,由于聚类时话者信息不足而影响切分准确度的问题,本文提出了一种基于进化隐马尔科夫模型和交叉对数似然比距离测度的多层次说话人分割聚类算法,在传统的话者分割聚类算法的基础上引入了重分割和重聚类的机制,以及基于距离测度和贝叶斯信息准则的分层聚类算法,有效的解决了传统方法中切分准确度受到话者信息制约的问题.在美国国家标准技术署(NIST)2003 Spring RT数据库上的实验结果表明,本文提出的算法比传统算法系统性能相对提高了41%.
