Example from Image Processing



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%matplotlib inline
import matplotlib.pyplot as plt

Here we'll take a look at a simple facial recognition example. This uses a dataset available within scikit-learn consisting of a subset of the Labeled Faces in the Wild data. Note that this is a relatively large download (~200MB) so it may take a while to execute.



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from sklearn import datasets
lfw_people = datasets.fetch_lfw_people(min_faces_per_person=70, resize=0.4,
                                       data_home='datasets')
lfw_people.data.shape

If you're on a unix-based system such as linux or Mac OSX, these shell commands can be used to see the downloaded dataset:



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!ls datasets



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!du -sh datasets/lfw_home

Once again, let's visualize these faces to see what we're working with:



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fig = plt.figure(figsize=(8, 6))
# plot several images
for i in range(15):
    ax = fig.add_subplot(3, 5, i + 1, xticks=[], yticks=[])
    ax.imshow(lfw_people.images[i], cmap=plt.cm.bone)



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import numpy as np
plt.figure(figsize=(10, 2))

unique_targets = np.unique(lfw_people.target)
counts = [(lfw_people.target == i).sum() for i in unique_targets]

plt.xticks(unique_targets, lfw_people.target_names[unique_targets])
locs, labels = plt.xticks()
plt.setp(labels, rotation=45, size=14)
_ = plt.bar(unique_targets, counts)

One thing to note is that these faces have already been localized and scaled to a common size. This is an important preprocessing piece for facial recognition, and is a process that can require a large collection of training data. This can be done in scikit-learn, but the challenge is gathering a sufficient amount of training data for the algorithm to work

Fortunately, this piece is common enough that it has been done. One good resource is OpenCV, the Open Computer Vision Library.

We'll perform a Support Vector classification of the images. We'll do a typical train-test split on the images to make this happen:



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from sklearn.cross_validation import train_test_split
X_train, X_test, y_train, y_test = train_test_split(
    lfw_people.data, lfw_people.target, random_state=0)

print(X_train.shape, X_test.shape)

Preprocessing: Principal Component Analysis

1850 dimensions is a lot for SVM. We can use PCA to reduce these 1850 features to a manageable size, while maintaining most of the information in the dataset. Here it is useful to use a variant of PCA called RandomizedPCA, which is an approximation of PCA that can be much faster for large datasets. We saw this method in the previous notebook, and will use it again here:



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from sklearn import decomposition
pca = decomposition.RandomizedPCA(n_components=150, whiten=True,
                                 random_state=1999)
pca.fit(X_train)
X_train_pca = pca.transform(X_train)
X_test_pca = pca.transform(X_test)
print(X_train_pca.shape)
print(X_test_pca.shape)

These projected components correspond to factors in a linear combination of component images such that the combination approaches the original face. In general, PCA can be a powerful technique for preprocessing that can greatly improve classification performance.

Doing the Learning: Support Vector Machines

Now we'll perform support-vector-machine classification on this reduced dataset:



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from sklearn import svm
clf = svm.SVC(C=5., gamma=0.001)
clf.fit(X_train_pca, y_train)

Finally, we can evaluate how well this classification did. First, we might plot a few of the test-cases with the labels learned from the training set:



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fig = plt.figure(figsize=(8, 6))
for i in range(15):
    ax = fig.add_subplot(3, 5, i + 1, xticks=[], yticks=[])
    ax.imshow(X_test[i].reshape((50, 37)), cmap=plt.cm.bone)
    y_pred = clf.predict(X_test_pca[i])[0]
    color = 'black' if y_pred == y_test[i] else 'red'
    ax.set_title(lfw_people.target_names[y_pred], fontsize='small', color=color)

The classifier is correct on an impressive number of images given the simplicity of its learning model! Using a linear classifier on 150 features derived from the pixel-level data, the algorithm correctly identifies a large number of the people in the images.

Again, we can quantify this effectiveness using clf.score



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print(clf.score(X_test_pca, y_test))

Final Note

Here we have used PCA "eigenfaces" as a pre-processing step for facial recognition. The reason we chose this is because PCA is a broadly-applicable technique, which can be useful for a wide array of data types. For more details on the eigenfaces approach, see the original paper by Turk and Penland, Eigenfaces for Recognition. Research in the field of facial recognition has moved much farther beyond this paper, and has shown specific feature extraction methods can be more effective. However, eigenfaces is a canonical example of machine learning "in the wild", and is a simple method with good results.



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