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.
出处
《计算机工程与设计》
CSCD
北大核心
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)
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
pattern recognition
hidden Markov model
continuous hidden Markov model
singular value decomposition
face recognition
