摘要
基于核函数的Fisher判别分析(KFD)在人脸识别中通常采用高斯径向基函数做核函数,但核函数中参数的选取对分类效果影响较大。目前参数的选取一般仅凭经验,且该方法在处理大样本时,速度较慢。针对这个问题,本文提出了一种融合小波变换和改进KFD的人脸识别的方法。该方法首先用小波变换降低样本的维数;然后在用KFD进行特征提取时,采用微粒群算法自动获取一个最优参数,增强分类效果;最后用SVM分类器完成特征的识别。实验表明,该方法与传统的KFD相比较,运算时间减少,而且识别率得到提高。
Gaussian radial basis function is usually applied as the kernel function of the kernel fisher discriminant analysis （KFD） in face recognition application. However, the parameter σ of the kernel function has a great impact on the classification. At present, the parameter is usually selected based on experience, and the process of KFD costs too much time for dealing with a large number of samples. To solve these problems, a method of face recognition is presented based on wavelet transform and improved KFD. It employs wavelet transform to compress the data of face image. And it applies PSO algorithm to automatically obtain the parameter to enhance the ability of classification when KFD is employed to complete feature extraction. Finally, support vector machine is used for classification. Numerical experimental results show that the method has a better operational efficiency and more accurate recognition rate than the traditional method of KFD.
出处
《光电工程》
CAS
CSCD
北大核心
2012年第3期94-99,共6页
Opto-Electronic Engineering
基金
中央高校基本科研业务科研专项(CDJXS10161114)
关键词
核函数:人脸识别
小波变换
微粒群算法
SVM分类器
kernel function
face recognition
wavelet transform
PSO algorithm
support vector machine
