简单的人脸分类实战

因为对人脸识别比较感兴趣，故利用python自带的fetch_lfw_people库，尝试做一下人脸分类，记录一下学习过程。

Example: Face Recognition 人脸分类

As an example of support vector machines in action, let’s take a look at the facial recognition problem.
We will use the Labeled Faces in the Wild dataset, which consists of several thousand collated photos of various public figures.
A fetcher for the dataset is built into Scikit-Learn:

#用下内置的数据集：人脸数据集
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)

Let’s plot a few of these faces to see what we’re working with:

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) #原始3000D==>150D
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  #用来模型的参数选择
param_grid = {'svc__C': [1, 5, 10],
              'svc__gamma': [0.0001, 0.0005, 0.001]}
grid = GridSearchCV(model, param_grid)

%time grid.fit(Xtrain, ytrain)
print(grid.best_params_)

Wall time: 30.4 s
{‘svc__C’: 5, ‘svc__gamma’: 0.001}

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

(337,)

看一下效果

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))

• 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  #混淆矩阵
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');

对角线表示预测的比较对的，非对角线表示容易将横纵坐标的人弄混

• 这样显示出来能帮助我们查看哪些人更容易弄混