sklearn.datasets.fetch_lfw_pairs(subset=’train’, data_home=None, funneled=True, resize=0.5, color=False, slice_=(slice(70, 195, None), slice(78, 172, None)), download_if_missing=True) [source]

Load the Labeled Faces in the Wild (LFW) pairs dataset (classification).

Download it if necessary.

Classes 5749
Samples total 13233
Dimensionality 5828
Features real, between 0 and 255

In the official README.txt this task is described as the “Restricted” task. As I am not sure as to implement the “Unrestricted” variant correctly, I left it as unsupported for now.

The original images are 250 x 250 pixels, but the default slice and resize arguments reduce them to 62 x 47.

Read more in the User Guide.

subset : optional, default: ‘train’

Select the dataset to load: ‘train’ for the development training set, ‘test’ for the development test set, and ‘10_folds’ for the official evaluation set that is meant to be used with a 10-folds cross validation.

data_home : optional, default: None

Specify another download and cache folder for the datasets. By default all scikit-learn data is stored in ‘~/scikit_learn_data’ subfolders.

funneled : boolean, optional, default: True

Download and use the funneled variant of the dataset.

resize : float, optional, default 0.5

Ratio used to resize the each face picture.

color : boolean, optional, default False

Keep the 3 RGB channels instead of averaging them to a single gray level channel. If color is True the shape of the data has one more dimension than the shape with color = False.

slice_ : optional

Provide a custom 2D slice (height, width) to extract the ‘interesting’ part of the jpeg files and avoid use statistical correlation from the background

download_if_missing : optional, True by default

If False, raise a IOError if the data is not locally available instead of trying to download the data from the source site.

The data is returned as a Bunch object with the following attributes:
data : numpy array of shape (2200, 5828). Shape depends on subset.

Each row corresponds to 2 ravel’d face images of original size 62 x 47 pixels. Changing the slice_, resize or subset parameters will change the shape of the output.

pairs : numpy array of shape (2200, 2, 62, 47). Shape depends on subset

Each row has 2 face images corresponding to same or different person from the dataset containing 5749 people. Changing the slice_, resize or subset parameters will change the shape of the output.

target : numpy array of shape (2200,). Shape depends on subset.

Labels associated to each pair of images. The two label values being different persons or the same person.

DESCR : string

Description of the Labeled Faces in the Wild (LFW) dataset.

© 2007–2018 The scikit-learn developers
Licensed under the 3-clause BSD License.