sklearn.datasets.fetch_olivetti_faces()

sklearn.datasets.fetch_olivetti_faces(data_home=None, shuffle=False, random_state=0, download_if_missing=True) [source]

Loader for the Olivetti faces data-set from AT&T.

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. shuffle : boolean, optional If True the order of the dataset is shuffled to avoid having images of the same person grouped. 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. random_state : optional, integer or RandomState object The seed or the random number generator used to shuffle the data.

Returns:

An object with the following attributes: : data : numpy array of shape (400, 4096) Each row corresponds to a ravelled face image of original size 64 x 64 pixels. images : numpy array of shape (400, 64, 64) Each row is a face image corresponding to one of the 40 subjects of the dataset. target : numpy array of shape (400, ) Labels associated to each face image. Those labels are ranging from 0-39 and correspond to the Subject IDs. DESCR : string Description of the modified Olivetti Faces Dataset.

Notes

This dataset consists of 10 pictures each of 40 individuals. The original database was available from (now defunct)

http://www.cl.cam.ac.uk/research/dtg/attarchive/facedatabase.html

The version retrieved here comes in MATLAB format from the personal web page of Sam Roweis:

http://www.cs.nyu.edu/~roweis/

Examples using sklearn.datasets.fetch_olivetti_faces

Face completion with a multi-output estimators

Online learning of a dictionary of parts of faces

Faces dataset decompositions

Pixel importances with a parallel forest of trees