基于FLDA、CPCA与HMM的人脸识别

为了获得具有较高识别率的算法,提出了一种将Fisher线性鉴别分析(Fisher Linear Discriminant Analysis)、复主分量分析(Principal Analysis in the Complex Space)与隐马尔可夫模型(Hidden Markov Models)相结合进行人脸识别的方法。对于输入的不同光照、人脸表情和姿势的图像先进行归一化处理,然后将归一化后的图像转化成一维向量,再用FLDA方法提取每幅图像的特征,形成新的复向量空间;通过运用复主分量分析,来抽取人脸图像的有效鉴别特征;最后通过HMM对这些特征进行训练,得到一个优化的HMM并应用于识别。在ORL人脸数据库中进行实验,实验结果表明,该方法具有较高的识别率。

FLDA,CPCA and HMM for face recognition

In order to obtain a better recognition rate,a new algorithm which focuses on the use of FLDA,CPCA and Hidden Markov Models for face recognition is presented.First,the differernt images are translated into one-dimension vector with the same mean and variance.Second,FLDA is used to get the features of the pictures and complex vector space,then CPCA is applied to get the new features and these new features are trained by HMMs.Finally,an optimized HMMs is obtained.Compared with other face recognition algorithms on the ORL face database,this method can get better recognition rate.