使用特征脸和支持向量机的人脸识别示例

本示例中使用的数据集是“Labeled Faces in the Wild”（LFW）的预处理摘录，也称为 LFW

http://vis-www.cs.umass.edu/lfw/lfw-funneled.tgz (233MB)

from time import time

import matplotlib.pyplot as plt
from scipy.stats import loguniform

from sklearn.datasets import fetch_lfw_people
from sklearn.decomposition import PCA
from sklearn.metrics import ConfusionMatrixDisplay, classification_report
from sklearn.model_selection import RandomizedSearchCV, train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.svm import SVC

下载数据（如果尚未下载），并将其作为 numpy 数组加载

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

# introspect the images arrays to find the shapes (for plotting)
n_samples, h, w = lfw_people.images.shape

# for machine learning we use the 2 data directly (as relative pixel
# positions info is ignored by this model)
X = lfw_people.data
n_features = X.shape[1]

# the label to predict is the id of the person
y = lfw_people.target
target_names = lfw_people.target_names
n_classes = target_names.shape[0]

print("Total dataset size:")
print("n_samples: %d" % n_samples)
print("n_features: %d" % n_features)
print("n_classes: %d" % n_classes)

Total dataset size:
n_samples: 1288
n_features: 1850
n_classes: 7

拆分为训练集和测试集，并保留 25% 的数据用于测试。

X_train, X_test, y_train, y_test = train_test_split(
    X, y, test_size=0.25, random_state=42
)

scaler = StandardScaler()
X_train = scaler.fit_transform(X_train)
X_test = scaler.transform(X_test)

在人脸数据集（视为未标记数据集）上计算 PCA（特征脸）：无监督特征提取/降维

n_components = 150

print(
    "Extracting the top %d eigenfaces from %d faces" % (n_components, X_train.shape[0])
)
t0 = time()
pca = PCA(n_components=n_components, svd_solver="randomized", whiten=True).fit(X_train)
print("done in %0.3fs" % (time() - t0))

eigenfaces = pca.components_.reshape((n_components, h, w))

print("Projecting the input data on the eigenfaces orthonormal basis")
t0 = time()
X_train_pca = pca.transform(X_train)
X_test_pca = pca.transform(X_test)
print("done in %0.3fs" % (time() - t0))

Extracting the top 150 eigenfaces from 966 faces
done in 0.097s
Projecting the input data on the eigenfaces orthonormal basis
done in 0.006s

训练支持向量机分类模型

print("Fitting the classifier to the training set")
t0 = time()
param_grid = {
    "C": loguniform(1e3, 1e5),
    "gamma": loguniform(1e-4, 1e-1),
}
clf = RandomizedSearchCV(
    SVC(kernel="rbf", class_weight="balanced"), param_grid, n_iter=10
)
clf = clf.fit(X_train_pca, y_train)
print("done in %0.3fs" % (time() - t0))
print("Best estimator found by grid search:")
print(clf.best_estimator_)

Fitting the classifier to the training set
done in 6.309s
Best estimator found by grid search:
SVC(C=76823.03433306456, class_weight='balanced', gamma=0.0034189458230957995)

对测试集上的模型质量进行定量评估

print("Predicting people's names on the test set")
t0 = time()
y_pred = clf.predict(X_test_pca)
print("done in %0.3fs" % (time() - t0))

print(classification_report(y_test, y_pred, target_names=target_names))
ConfusionMatrixDisplay.from_estimator(
    clf, X_test_pca, y_test, display_labels=target_names, xticks_rotation="vertical"
)
plt.tight_layout()
plt.show()

Predicting people's names on the test set
done in 0.049s
                   precision    recall  f1-score   support

     Ariel Sharon       0.75      0.69      0.72        13
     Colin Powell       0.72      0.87      0.79        60
  Donald Rumsfeld       0.77      0.63      0.69        27
    George W Bush       0.88      0.95      0.91       146
Gerhard Schroeder       0.95      0.80      0.87        25
      Hugo Chavez       0.90      0.60      0.72        15
       Tony Blair       0.93      0.75      0.83        36

         accuracy                           0.84       322
        macro avg       0.84      0.75      0.79       322
     weighted avg       0.85      0.84      0.84       322

使用 matplotlib 对预测结果进行定性评估

def plot_gallery(images, titles, h, w, n_row=3, n_col=4):
    """Helper function to plot a gallery of portraits"""
    plt.figure(figsize=(1.8 * n_col, 2.4 * n_row))
    plt.subplots_adjust(bottom=0, left=0.01, right=0.99, top=0.90, hspace=0.35)
    for i in range(n_row * n_col):
        plt.subplot(n_row, n_col, i + 1)
        plt.imshow(images[i].reshape((h, w)), cmap=plt.cm.gray)
        plt.title(titles[i], size=12)
        plt.xticks(())
        plt.yticks(())

绘制测试集一部分上的预测结果

def title(y_pred, y_test, target_names, i):
    pred_name = target_names[y_pred[i]].rsplit(" ", 1)[-1]
    true_name = target_names[y_test[i]].rsplit(" ", 1)[-1]
    return "predicted: %s\ntrue:      %s" % (pred_name, true_name)


prediction_titles = [
    title(y_pred, y_test, target_names, i) for i in range(y_pred.shape[0])
]

plot_gallery(X_test, prediction_titles, h, w)

绘制最显著的特征脸库

eigenface_titles = ["eigenface %d" % i for i in range(eigenfaces.shape[0])]
plot_gallery(eigenfaces, eigenface_titles, h, w)

plt.show()

通过训练卷积神经网络可以更有效地解决人脸识别问题，但这类模型超出了 scikit-learn 库的范围。感兴趣的读者可以尝试使用 pytorch 或 tensorflow 来实现此类模型。

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