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scikit-learn 1.3.2
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# 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)

    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

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

    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

Split into a training set and a test and keep 25% of the data for testing.

    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)

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

    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 0.666s
    Projecting the input data on the eigenfaces orthonormal basis
    done in 0.031s

Train a SVM classification model

    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 3.892s
    Best estimator found by grid search:
    SVC(C=76823.03433306457, class_weight='balanced', gamma=0.0034189458230957995)

Quantitative evaluation of the model quality on the test set

    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 0.038s
                       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

Qualitative evaluation of the predictions using matplotlib

    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(())

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()

Face recognition problem would be much more effectively solved by training convolutional neural networks but this family of models is outside of the scope of the scikit-learn library. Interested readers should instead try to use pytorch or tensorflow to implement such models.

Total running time of the script: (6 minutes 29.604 seconds)

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