摘要
将Gabor小波变换和支持向量机分类方法结合起来进行人脸识别。通过由Gabor小波变换系数表示的若干个人脸模板和人脸图像之间的匹配来确定特征点的近似位置;在所有的特征点位置计算Gabor小波变换系数并将其串联成表示人脸图像的向量;采用一种层次分解的支持向量机二叉决策树进行分类识别。实验结果表明了该方法的可行性。
This paper combines the Gabor wavelet transform with support vector machines classification method for face recognition. Facial feature points are located roughly through the matching between certain face templates which are represented by Gabor wavelet transform coefficients and a face image. Gabor wavelet transform coefficients are extracted at every facial feature points and these coefficients are catenated into a vector to represent a face image. A hierarchical decomposed support vector machines binary decision tree is used for classification. Experimental results show the feasibility of the proposed face recognition method.
出处
《计算机工程》
EI
CAS
CSCD
北大核心
2006年第19期181-182,226,共3页
Computer Engineering
