6-2基于SVM的人脸识别实战

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人脸识别

作为SVM实际的一个应用，让我们看一下人脸识别问题。将使用sklearn数据集中带有标签的人脸图片

from sklearn.datasets import fetch_lfw_people

faces = fetch_lfw_people(min_faces_per_person=60)
print(faces.target_names)
print(faces.images.shape)

['Ariel Sharon' 'Colin Powell' 'Donald Rumsfeld' 'George W Bush'
 'Gerhard Schroeder' 'Hugo Chavez' 'Junichiro Koizumi' 'Tony Blair']
(1348, 62, 47)

画出一些我们正在使用的图片

import matplotlib.pyplot as plt
fig, ax = plt.subplots(3, 5)
for i, axi in enumerate(ax.flat):
    axi.imshow(faces.images[i], cmap='bone')
    axi.set(xticks=[], yticks=[],
            xlabel=faces.target_names[faces.target[i]])

• 每个图的大小是 [62×47]
• 在这里我们就把每一个像素点当成了一个特征，但是这样特征太多了，用PCA降维一下吧！

from sklearn.svm import SVC
#from sklearn.decomposition import RandomizedPCA
from sklearn.decomposition import PCA
from sklearn.pipeline import make_pipeline

pca = PCA(n_components=150, whiten=True, random_state=42)
svc = SVC(kernel='rbf', class_weight='balanced')
model = make_pipeline(pca, svc)

from sklearn.model_selection import train_test_split
Xtrain, Xtest, ytrain, ytest = train_test_split(faces.data, faces.target,
                                                random_state=40)

使用grid search cross-validation来选择我们的参数

from sklearn.model_selection import GridSearchCV

param_grid = {'svc__C': [1, 5, 10], 'svc__gamma': [0.0001, 0.0005, 0.001]}
grid = GridSearchCV(model, param_grid)
grid.fit(Xtrain, ytrain)
print(grid.best_params_)

C:\ProgramData\Anaconda3\lib\site-packages\sklearn\model_selection\_split.py:2053: FutureWarning: You should specify a value for 'cv' instead of relying on the default value. The default value will change from 3 to 5 in version 0.22.
  warnings.warn(CV_WARNING, FutureWarning)
C:\ProgramData\Anaconda3\lib\site-packages\sklearn\model_selection\_search.py:841: DeprecationWarning: The default of the `iid` parameter will change from True to False in version 0.22 and will be removed in 0.24. This will change numeric results when test-set sizes are unequal.
  DeprecationWarning)


{'svc__C': 5, 'svc__gamma': 0.001}

model = grid.best_estimator_
yfit = model.predict(Xtest)
yfit.shape

(337,)

fig, ax = plt.subplots(4, 6)
for i, axi in enumerate(ax.flat):
    axi.imshow(Xtest[i].reshape(62, 47), cmap='bone')
    axi.set(xticks=[], yticks=[])
    axi.set_ylabel(faces.target_names[yfit[i]].split()[-1],
                   color='black' if yfit[i] == ytest[i] else 'red')
fig.suptitle('Predicted Names; Incorrect Labels in Red', size=14);

from sklearn.metrics import classification_report
print(classification_report(ytest, yfit,
                            target_names=faces.target_names))

                   precision    recall  f1-score   support

     Ariel Sharon       0.50      0.50      0.50        16
     Colin Powell       0.69      0.81      0.75        54
  Donald Rumsfeld       0.83      0.85      0.84        34
    George W Bush       0.94      0.88      0.91       136
Gerhard Schroeder       0.72      0.85      0.78        27
      Hugo Chavez       0.81      0.72      0.76        18
Junichiro Koizumi       0.87      0.87      0.87        15
       Tony Blair       0.85      0.76      0.80        37

        micro avg       0.82      0.82      0.82       337
        macro avg       0.78      0.78      0.78       337
     weighted avg       0.83      0.82      0.82       337

• 精度(precision) = 正确预测的个数(TP)/被预测正确的个数(TP+FP)
• 召回率(recall)=正确预测的个数(TP)/预测个数(TP+FN)
• F1 = 2精度召回率/(精度+召回率)

from sklearn.metrics import confusion_matrix
import seaborn as sns
mat = confusion_matrix(ytest, yfit)
sns.heatmap(mat.T, square=True, annot=True, fmt='d', cbar=False,
            xticklabels=faces.target_names,
            yticklabels=faces.target_names)
plt.xlabel('true label')
plt.ylabel('predicted label');