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# `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 str or path-like, 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 bool, default=True

      Download and use the funneled variant of the dataset.

    resize float or None, default=0.5

      Ratio used to resize the each face picture. If `None`, no resizing is performed.

    min_faces_per_person int, default=None

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

    color bool, 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_tuple of slice, default=(slice(70, 195), slice(78, 172))

      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 bool, default=True

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

    return_X_y bool, 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 `Bunch`

      Dictionary-like object, with the following attributes.

      datanumpy 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.

      imagesnumpy 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.

      targetnumpy array of shape (13233,)

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

      target_namesnumpy array of shape (5749,)

        Names of all persons in the dataset. Position in array corresponds to the person ID in the target array.

      DESCRstr

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

    (data, target)tuple if `return_X_y` is True

      A tuple of two ndarray. The first containing a 2D array of shape (n_samples, n_features) with each row representing one sample and each column representing the features. The second ndarray of shape (n_samples,) containing the target samples.

      New in version 0.20.

## Examples using `sklearn.datasets.fetch_lfw_people`

Faces recognition example using eigenfaces and SVMs

Faces recognition example using eigenfaces and SVMs