使用 Haar 特征描述符进行人脸分类

Haar 特征描述符已成功用于实现第一个实时人脸检测器[1]。受此应用的启发，我们提出了一个示例，说明了 Haar 特征的提取、选择和分类以检测人脸与非人脸。

注释

此示例依赖于 scikit-learn 进行特征选择和分类。

参考文献

[1]

Viola, Paul 和 Michael J. Jones。“稳健的实时人脸检测。”国际计算机视觉杂志 57.2 (2004)：137-154。https://www.merl.com/publications/docs/TR2004-043.pdf DOI:10.1109/CVPR.2001.990517

from time import time

import numpy as np
import matplotlib.pyplot as plt

from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import train_test_split
from sklearn.metrics import roc_auc_score

from skimage.data import lfw_subset
from skimage.transform import integral_image
from skimage.feature import haar_like_feature
from skimage.feature import haar_like_feature_coord
from skimage.feature import draw_haar_like_feature

从图像中提取 Haar 特征的过程相对简单。首先，定义一个感兴趣区域 (ROI)。其次，计算此 ROI 内的积分图像。最后，使用积分图像提取特征。

def extract_feature_image(img, feature_type, feature_coord=None):
    """Extract the haar feature for the current image"""
    ii = integral_image(img)
    return haar_like_feature(
        ii,
        0,
        0,
        ii.shape[0],
        ii.shape[1],
        feature_type=feature_type,
        feature_coord=feature_coord,
    )

我们使用 CBCL 数据集的一个子集，该数据集由 100 张人脸图像和 100 张非人脸图像组成。每张图像都已调整大小为 19 x 19 像素的 ROI。我们从每个组中选择 75 张图像来训练分类器并确定最显着的特征。每个类别剩余的 25 张图像用于评估分类器的性能。

images = lfw_subset()
# To speed up the example, extract the two types of features only
feature_types = ['type-2-x', 'type-2-y']

# Compute the result
t_start = time()
X = [extract_feature_image(img, feature_types) for img in images]
X = np.stack(X)
time_full_feature_comp = time() - t_start

# Label images (100 faces and 100 non-faces)
y = np.array([1] * 100 + [0] * 100)

X_train, X_test, y_train, y_test = train_test_split(
    X, y, train_size=150, random_state=0, stratify=y
)

# Extract all possible features
feature_coord, feature_type = haar_like_feature_coord(
    width=images.shape[2], height=images.shape[1], feature_type=feature_types
)

可以训练随机森林分类器以选择最显着的特征，特别是用于人脸分类。其思想是确定树集成最常使用的特征。通过在后续步骤中仅使用最显着的特征，我们可以显着加快计算速度，同时保持准确性。

# Train a random forest classifier and assess its performance
clf = RandomForestClassifier(
    n_estimators=1000, max_depth=None, max_features=100, n_jobs=-1, random_state=0
)
t_start = time()
clf.fit(X_train, y_train)
time_full_train = time() - t_start
auc_full_features = roc_auc_score(y_test, clf.predict_proba(X_test)[:, 1])

# Sort features in order of importance and plot the six most significant
idx_sorted = np.argsort(clf.feature_importances_)[::-1]

fig, axes = plt.subplots(3, 2)
for idx, ax in enumerate(axes.ravel()):
    image = images[0]
    image = draw_haar_like_feature(
        image, 0, 0, images.shape[2], images.shape[1], [feature_coord[idx_sorted[idx]]]
    )
    ax.imshow(image)
    ax.set_xticks([])
    ax.set_yticks([])

_ = fig.suptitle('The most important features')

我们可以通过检查特征重要性的累积和来选择最重要的特征。在此示例中，我们保留了代表累积值 70% 的特征（这对应于仅使用特征总数的 3%）。

cdf_feature_importances = np.cumsum(clf.feature_importances_[idx_sorted])
cdf_feature_importances /= cdf_feature_importances[-1]  # divide by max value
sig_feature_count = np.count_nonzero(cdf_feature_importances < 0.7)
sig_feature_percent = round(sig_feature_count / len(cdf_feature_importances) * 100, 1)
print(
    f'{sig_feature_count} features, or {sig_feature_percent}%, '
    f'account for 70% of branch points in the random forest.'
)

# Select the determined number of most informative features
feature_coord_sel = feature_coord[idx_sorted[:sig_feature_count]]
feature_type_sel = feature_type[idx_sorted[:sig_feature_count]]
# Note: it is also possible to select the features directly from the matrix X,
# but we would like to emphasize the usage of `feature_coord` and `feature_type`
# to recompute a subset of desired features.

# Compute the result
t_start = time()
X = [extract_feature_image(img, feature_type_sel, feature_coord_sel) for img in images]
X = np.stack(X)
time_subs_feature_comp = time() - t_start

y = np.array([1] * 100 + [0] * 100)
X_train, X_test, y_train, y_test = train_test_split(
    X, y, train_size=150, random_state=0, stratify=y
)

712 features, or 0.7%, account for 70% of branch points in the random forest.

提取特征后，我们可以训练和测试一个新的分类器。

t_start = time()
clf.fit(X_train, y_train)
time_subs_train = time() - t_start

auc_subs_features = roc_auc_score(y_test, clf.predict_proba(X_test)[:, 1])

summary = (
    f'Computing the full feature set took '
    f'{time_full_feature_comp:.3f}s, '
    f'plus {time_full_train:.3f}s training, '
    f'for an AUC of {auc_full_features:.2f}. '
    f'Computing the restricted feature set took '
    f'{time_subs_feature_comp:.3f}s, plus {time_subs_train:.3f}s '
    f'training, for an AUC of {auc_subs_features:.2f}.'
)

print(summary)
plt.show()

Computing the full feature set took 33.047s, plus 1.637s training, for an AUC of 1.00. Computing the restricted feature set took 0.098s, plus 1.362s training, for an AUC of 1.00.