Faces recognition (using "Eigenfaces", ie PCA features)

The dataset used in this example is a preprocessed excerpt of the "Labeled Faces in the Wild", aka LFW_:

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

.. _LFW: http://vis-www.cs.umass.edu/lfw/

The goal of this micro-project is to tune image feature extraction, and learn with at least two classification algorithms using somewhat optimal hyper-parameters, and compare achievable recognition performances.

In [63]:
%matplotlib inline

from __future__ import print_function

from time import time
import logging
import matplotlib.pyplot as plt

from sklearn.model_selection import train_test_split
from sklearn.model_selection import GridSearchCV
from sklearn.datasets import fetch_lfw_people
from sklearn.metrics import classification_report
from sklearn.metrics import confusion_matrix
from sklearn.decomposition import PCA
from sklearn.svm import SVC


print(__doc__)

# Display progress logs on stdout
logging.basicConfig(level=logging.INFO, format='%(asctime)s %(message)s')

Automatically created module for IPython interactive environment

Download the data, if not already on disk and load it as numpy arrays

In [64]:
lfw_people = fetch_lfw_people(data_home='.', 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)

2017-05-12 15:34:48,546 Loading LFW people faces from .\lfw_home

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

Split into a training set and a test set using a stratified k fold

In [65]:
# split into a training and testing set
X_train, X_test, y_train, y_test = train_test_split(
    X, y, test_size=0.25, random_state=42)

Compute a PCA (eigenfaces) on the face dataset (treated as unlabeled dataset): unsupervised feature extraction / dimensionality reduction

In [66]:
n_components = 50

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 50 eigenfaces from 966 faces
done in 0.159s
Projecting the input data on the eigenfaces orthonormal basis
done in 0.024s

Train a classifier (initially a Random Forest) on PCA features

In [67]:
from sklearn.ensemble import RandomForestClassifier
clf = RandomForestClassifier(n_estimators=90)
clf.fit(X_train_pca, y_train)
Out[67]:
RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',
            max_depth=None, max_features='auto', max_leaf_nodes=None,
            min_impurity_split=1e-07, min_samples_leaf=1,
            min_samples_split=2, min_weight_fraction_leaf=0.0,
            n_estimators=90, n_jobs=1, oob_score=False, random_state=None,
            verbose=0, warm_start=False)

Quantitative evaluation of the model quality on the test set

In [68]:
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))
print(confusion_matrix(y_test, y_pred, labels=range(n_classes)))

Predicting people's names on the test set
done in 0.093s
                   precision    recall  f1-score   support

     Ariel Sharon       1.00      0.08      0.14        13
     Colin Powell       0.80      0.65      0.72        60
  Donald Rumsfeld       0.86      0.22      0.35        27
    George W Bush       0.61      0.99      0.75       146
Gerhard Schroeder       0.90      0.36      0.51        25
      Hugo Chavez       0.50      0.20      0.29        15
       Tony Blair       0.80      0.22      0.35        36

      avg / total       0.72      0.66      0.60       322

[[  1   1   1  10   0   0   0]
 [  0  39   0  21   0   0   0]
 [  0   2   6  19   0   0   0]
 [  0   0   0 145   0   1   0]
 [  0   2   0  10   9   2   2]
 [  0   1   0  10   1   3   0]
 [  0   4   0  24   0   0   8]]

Qualitative evaluation of the predictions using matplotlib

In [69]:
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=.01, right=.99, top=.90, hspace=.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(())


# plot the result of the prediction on a portion of the test set

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)

# plot the gallery of the most significative eigenfaces

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

plt.show()
In [ ]: