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使用特徵臉和 SVM 的人臉辨識範例#
此範例中使用的資料集是「Labeled Faces in the Wild」的預處理摘錄，也稱為 LFW：http://vis-www.cs.umass.edu/lfw/lfw-funneled.tgz (233MB)
# Authors: The scikit-learn developers
# SPDX-License-Identifier: BSD-3-Clause
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
下載資料，如果尚未在磁碟上，並將其載入為 numpy 陣列
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
分割為訓練集和測試集，並保留 25% 的資料用於測試。
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)
在臉部資料集上計算 PCA（特徵臉）（被視為未標記的資料集）：無監督特徵提取/降維
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 0.087s
Projecting the input data on the eigenfaces orthonormal basis
done in 0.006s
訓練 SVM 分類模型
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 5.919s
Best estimator found by grid search:
SVC(C=np.float64(76823.03433306457), class_weight='balanced',
gamma=np.float64(0.0034189458230957995))
測試集上模型品質的定量評估
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 0.045s
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 對預測進行定性評估
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(())
繪製測試集部分資料的預測結果
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)
繪製最具意義的特徵臉圖庫
eigenface_titles = ["eigenface %d" % i for i in range(eigenfaces.shape[0])]
plot_gallery(eigenfaces, eigenface_titles, h, w)
plt.show()
人臉辨識問題可以透過訓練卷積神經網路更有效地解決，但這類模型不在 scikit-learn 函式庫的範圍內。有興趣的讀者應嘗試使用 pytorch 或 tensorflow 來實作此類模型。
腳本的總執行時間： (0 分鐘 6.862 秒)
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