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+.. _labeled_faces_in_the_wild_dataset:
+
+The Labeled Faces in the Wild face recognition dataset
+------------------------------------------------------
+
+This dataset is a collection of JPEG pictures of famous people collected
+over 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. The typical task is called
+Face Verification: given a pair of two pictures, a binary classifier
+must predict whether the two images are from the same person.
+
+An alternative task, Face Recognition or Face Identification is:
+given the picture of the face of an unknown person, identify the name
+of the person by referring to a gallery of previously seen pictures of
+identified persons.
+
+Both Face Verification and Face Recognition are tasks that are typically
+performed on the output of a model trained to perform Face Detection. The
+most popular model for Face Detection is called Viola-Jones and is
+implemented in the OpenCV library. The LFW faces were extracted by this
+face detector from various online websites.
+
+**Data Set Characteristics:**
+
+    =================   =======================
+    Classes                                5749
+    Samples total                         13233
+    Dimensionality                         5828
+    Features            real, between 0 and 255
+    =================   =======================
+
+Usage
+~~~~~
+
+``scikit-learn`` provides two loaders that will automatically download,
+cache, parse the metadata files, decode the jpeg and convert the
+interesting slices into memmapped numpy arrays. This dataset size is more
+than 200 MB. The first load typically takes more than a couple of minutes
+to fully decode the relevant part of the JPEG files into numpy arrays. If
+the dataset has  been loaded once, the following times the loading times
+less than 200ms by using a memmapped version memoized on the disk in the
+``~/scikit_learn_data/lfw_home/`` folder using ``joblib``.
+
+The first loader is used for the Face Identification task: a multi-class
+classification task (hence supervised learning)::
+
+  >>> from sklearn.datasets import fetch_lfw_people
+  >>> lfw_people = fetch_lfw_people(min_faces_per_person=70, resize=0.4)
+
+  >>> for name in lfw_people.target_names:
+  ...     print(name)
+  ...
+  Ariel Sharon
+  Colin Powell
+  Donald Rumsfeld
+  George W Bush
+  Gerhard Schroeder
+  Hugo Chavez
+  Tony Blair
+
+The default slice is a rectangular shape around the face, removing
+most of the background::
+
+  >>> lfw_people.data.dtype
+  dtype('float32')
+
+  >>> lfw_people.data.shape
+  (1288, 1850)
+
+  >>> lfw_people.images.shape
+  (1288, 50, 37)
+
+Each of the ``1140`` faces is assigned to a single person id in the ``target``
+array::
+
+  >>> lfw_people.target.shape
+  (1288,)
+
+  >>> list(lfw_people.target[:10])
+  [5, 6, 3, 1, 0, 1, 3, 4, 3, 0]
+
+The second loader is typically used for the face verification task: each sample
+is a pair of two picture belonging or not to the same person::
+
+  >>> from sklearn.datasets import fetch_lfw_pairs
+  >>> lfw_pairs_train = fetch_lfw_pairs(subset='train')
+
+  >>> list(lfw_pairs_train.target_names)
+  ['Different persons', 'Same person']
+
+  >>> lfw_pairs_train.pairs.shape
+  (2200, 2, 62, 47)
+
+  >>> lfw_pairs_train.data.shape
+  (2200, 5828)
+
+  >>> lfw_pairs_train.target.shape
+  (2200,)
+
+Both for the :func:`sklearn.datasets.fetch_lfw_people` and
+:func:`sklearn.datasets.fetch_lfw_pairs` function it is
+possible to get an additional dimension with the RGB color channels by
+passing ``color=True``, in that case the shape will be
+``(2200, 2, 62, 47, 3)``.
+
+The :func:`sklearn.datasets.fetch_lfw_pairs` datasets is subdivided into
+3 subsets: the development ``train`` set, the development ``test`` set and
+an evaluation ``10_folds`` set meant to compute performance metrics using a
+10-folds cross validation scheme.
+
+.. topic:: References:
+
+ * `Labeled Faces in the Wild: A Database for Studying Face Recognition
+   in Unconstrained Environments.
+   <http://vis-www.cs.umass.edu/lfw/lfw.pdf>`_
+   Gary B. Huang, Manu Ramesh, Tamara Berg, and Erik Learned-Miller.
+   University of Massachusetts, Amherst, Technical Report 07-49, October, 2007.
+
+
+Examples
+~~~~~~~~
+
+:ref:`sphx_glr_auto_examples_applications_plot_face_recognition.py`