sklearn.datasets.fetch_lfw_people

sklearn.datasets.fetch_lfw_people(data_home=None, funneled=True, resize=0.5, min_faces_per_person=0, color=False, slice_=(slice(70, 195, None), slice(78, 172, None)), download_if_missing=True, return_X_y=False)

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

Download it if necessary.

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

Read more in the User Guide.

Parameters:

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.

min_faces_per_person : int, optional, default None

The extracted dataset will only retain pictures of people that have at least min_faces_per_person different pictures.

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.

return_X_y : boolean, default=False.

If True, returns (dataset.data, dataset.target) instead of a Bunch object. See below for more information about the dataset.data and dataset.target object.

New in version 0.20.

Returns:

dataset : dict-like object with the following attributes:

dataset.data : numpy array of shape (13233, 2914)

Each row corresponds to a ravelled face image of original size 62 x 47 pixels. Changing the slice_ or resize parameters will change the shape of the output.

dataset.images : numpy array of shape (13233, 62, 47)

Each row is a face image corresponding to one of the 5749 people in the dataset. Changing the slice_ or resize parameters will change the shape of the output.

dataset.target : numpy array of shape (13233,)

Labels associated to each face image. Those labels range from 0-5748 and correspond to the person IDs.

dataset.DESCR : string

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

(data, target) : tuple if return_X_y is True

New in version 0.20.

Examples using sklearn.datasets.fetch_lfw_people

Faces recognition example using eigenfaces and SVMs