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) [source]

Loader for the Labeled Faces in the Wild (LFW) people dataset

This dataset is a collection of JPEG pictures of famous people collected on the internet, all details are available on the official website:

http://vis-www.cs.umass.edu/lfw/

Each picture is centered on a single face. Each pixel of each channel (color in RGB) is encoded by a float in range 0.0 - 1.0.

The task is called Face Recognition (or Identification): given the picture of a face, find the name of the person given a training set (gallery).

The original images are 250 x 250 pixels, but the default slice and resize arguments reduce them to 62 x 74.

Parameters:

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.
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_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.

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.