基于模糊准则的小波特征选择在人脸识别中的应用

Fuzzy Criterion Based Wavelet Feature Selection for Face Recognition

提出一种基于模糊准则的小波特征选择方法来实现人脸识别.首先,利用模糊准则得到最优小波包分解;其次,亦利用模糊准则对最优小波包分解中特征(小波系数)的分类能力进行评价并排序;再次,选择鉴别能力强的特征并将它们输入到EFM模型以实现降维,并使用基于最小二乘误差的线性鉴别函数实现分类.人脸识别实验结果表明基于模糊准则的小波特征选择方法的识别率要高于主元分析(PCA)算法.*

This paper proposes a fuzzy criterion based wavelet feature selection method for face recognition. Firstly, the optimal WPD (wavelet packet decomposition) with the fuzzy criterion is obtained. Secondly, the classification abilities of the features(WP coefficients) in the optimal WPD are also evaluated and ranked by the fuzzy criterion. Thirdly, the discriminant features are selected and input into EFM(enhanced fisher linear discriminant model) for the purpose of dimension reduction, and the classification with linear discriminant functions based on least square error is made. Finally, the experiment on face recognition indicates that the recognition rates achieved by fuzzy based wavelet feature selection are higher than those obtained by PCA(principal component analysis) method.