摘要
在人脸识别领域,如何提取人脸特征和降低特征维数是关键.提出了一种基于小波包变换和主元分析相结合的人脸识别方法.小波包具有能够保留图像的主体信息又保留不同方向细节信息的优点.算法首先利用小波包变换,把人脸图像分解成不同尺度的低频和高频部分,提取最优基,再采用PCA方法进行人脸的识别.在ORL人脸数据库的仿真结果表明,该算法能有效提高人脸识别性能,具有较高识别率.
It＇s a key problem to abtain appropriately low-dimensioned face features in the face recognition.A face recognition method bas;ed on wavelet packet and Principal Component Analysis（PCA）is presented.Wavelet packet transform can resave the main information and details in different level.Firstly,Wavelet packet transform decomposed an image into low frequency hand and high frequency hand.Than,chooses the best-basis.Lastly,recognizes with the image based on PCA.The experimental results on Yale data-bases demonstrate the high efficiency of the algorithm in runtime and correct localization rate.
出处
《微电子学与计算机》
CSCD
北大核心
2011年第1期92-94,98,共4页
Microelectronics & Computer
基金
广东省自然科学基金项目(101754539192000000)
