.. _sphx_glr_auto_examples_applications_face_recognition.py:


===================================================
Faces recognition example using eigenfaces and SVMs
===================================================

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/

Expected results for the top 5 most represented people in the dataset:

================== ============ ======= ========== =======
                   precision    recall  f1-score   support
================== ============ ======= ========== =======
     Ariel Sharon       0.67      0.92      0.77        13
     Colin Powell       0.75      0.78      0.76        60
  Donald Rumsfeld       0.78      0.67      0.72        27
    George W Bush       0.86      0.86      0.86       146
Gerhard Schroeder       0.76      0.76      0.76        25
      Hugo Chavez       0.67      0.67      0.67        15
       Tony Blair       0.81      0.69      0.75        36

      avg / total       0.80      0.80      0.80       322
================== ============ ======= ========== =======



.. code-block:: python

    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')



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


.. code-block:: python


    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)



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


.. code-block:: python


    # 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


.. code-block:: python

    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))



Train a SVM classification model


.. code-block:: python


    print("Fitting the classifier to the training set")
    t0 = time()
    param_grid = {'C': [1e3, 5e3, 1e4, 5e4, 1e5],
                  'gamma': [0.0001, 0.0005, 0.001, 0.005, 0.01, 0.1], }
    clf = GridSearchCV(SVC(kernel='rbf', class_weight='balanced'), param_grid)
    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_)



Quantitative evaluation of the model quality on the test set


.. code-block:: python


    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)))



Qualitative evaluation of the predictions using matplotlib


.. code-block:: python


    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()

**Total running time of the script:**
(0 minutes 0.000 seconds)



.. container:: sphx-glr-download

    **Download Python source code:** :download:`face_recognition.py <face_recognition.py>`


.. container:: sphx-glr-download

    **Download IPython notebook:** :download:`face_recognition.ipynb <face_recognition.ipynb>`