sklearn实现人脸识别lda特征提取

首先，我们需要导入必要的库：

import numpy as np
from sklearn.discriminant_analysis import LinearDiscriminantAnalysis as LDA
from sklearn.datasets import fetch_lfw_people
from sklearn.model_selection import train_test_split
from sklearn.svm import SVC
from sklearn.metrics import classification_report

接着，我们需要获取人脸数据集，这里我选择了LFW（Labeled Faces in the Wild）数据集：

lfw_people = fetch_lfw_people(min_faces_per_person=70, resize=0.4)

然后，我们需要准备好数据集，将数据集分为训练集和测试集，并将数据集进行LDA特征提取：

X = lfw_people.data
y = lfw_people.target

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3)

lda = LDA(n_components=100)
X_train_lda = lda.fit_transform(X_train, y_train)
X_test_lda = lda.transform(X_test)

在进行LDA特征提取后，我们可以使用SVM进行分类：

clf = SVC(kernel='rbf', class_weight='balanced')
clf.fit(X_train_lda, y_train)

y_pred = clf.predict(X_test_lda)
print(classification_report(y_test, y_pred, target_names=lfw_people.target_names))

最后，我们可以输出分类报告，得到人脸识别的结果。