sklearn.datasets.fetch_lfw_pairs

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.

Parameters:

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.

Returns:

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.