基于连续隐马尔可夫模型的人脸识别方法  

Approach of face recognition based on continuous hidden Markov model

作　　者：沈杰[1] 王正群[1] 邹军[1] 侯艳平[1] 

机构地区：[1]扬州大学信息工程学院,江苏扬州225009

出　　处：《计算机工程与设计》2008年第3期707-709,718,共4页Computer Engineering and Design

基　　金：江苏省高校自然科学基金项目(05KJB520102;07KJB520133);扬州大学自然科学基金项目(KK0413160)

摘　　要：提出了一种基于连续隐马尔可夫模型的人脸图像识别方法,主要内容包括以下方面:①由于奇异值向量具有稳定性、转置不变性等特点,对归一化的人脸图像,采用奇异值分解抽取人脸图像特征作为观察值序列;②在人脸识别中应用连续隐马尔可夫模型,采用双高斯概率密度函数训练、建立HMM模型,再利用建好的HMM模型进行识别。实验结果显示,所提出的方法减少了数据计算量,运行速度快,并提高了识别率,完全满足人脸识别系统实时性要求。A human face recognition approach based on the continuous hidden Markov model is proposed, its essential contents list as follows： （1）With the stability and the transposes the invariability, singular value decomposition is used to extract the character of the face image as the observed value array for the normalized face image. （2）In the face automatic recognition system, a continuous HMM method is used to recognize face and the two Gaussian pdf is adopted to build up the HMM model, and then the model could be used in identifying face. The results ofexperiments show that the method proposed reduces the computing time, runs faster, and the recognition rate is increased, so it can satisfy the needs of real-time face recognition.

关 键 词：模式识别 隐马尔可夫模型 连续隐马尔可夫模型 奇异值分解 人脸识别 

分 类 号：TP391.4[自动化与计算机技术—计算机应用技术]