摘要
运用小波进行图像分解提取低频子带图,并利用优化的线性判别分析(LDA)算法寻找最优投影子空间,从而映射提取人脸特征,实现人脸的分类识别。该方法避免了传统LDA算法中类内离散度矩阵非奇异的要求,解决了边缘类重叠问题,具有更广泛的应用空间。实验表明:该方法优于传统的LDA方法和主分量分析(PCA)方法。
Low frequency sub-band figures are extracted with wavelet transform,the optimal cast shadow space is found by using optimized linear discriminant analysis（LDA）algorithm,the optimal feature space is got.Face classification and identification are realized in the feature space.In this method,nonsingularity of within class scatter matrix became unnecessary,and the problem of edge overlap is also solved.So,it has better generalization ability comparing with traditional LDA algorithm.Experimental results show that this method is superior to the tranditional LDA and principle component analysis（PCA） method.
出处
《传感器与微系统》
CSCD
北大核心
2012年第5期65-67,共3页
Transducer and Microsystem Technologies
基金
中央高校基本科研业务费资助项目(CDJZR10160011)
重庆市自然科学基金资助项目(2010BB2049)
关键词
小波变换
线性判别分析
特征提取
人脸识别
wavelet transform
linear discriminant analysis（LDA）
feature extraction
face recognition
