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LDA线性判别分析提取人脸特征

于 2022-11-27 发布 文件大小:3.33 MB
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LDA线性判别分析是一种经典的提取特征的算法,它的基本思想是通过样本的类内离散度和类间离散度,寻找由最佳投影矢量构成的投影矩阵。将原始的样本数据投影到特征子空间中,实现数据分类。由于在人脸识别时常常会遇到小样本问题,因此在本次代码中,先用PCA主成分分析的方法降低样本维数,再用线性判别分析提取特征。最后,使用最小距离分类器判别分类,得到一个投影维度和识别率的曲线图。

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