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<ul>
<li><a class="reference internal" href="#"><code class="docutils literal"><span class="pre">sklearn.datasets</span></code>.fetch_lfw_people</a><ul>
<li><a class="reference internal" href="#examples-using-sklearn-datasets-fetch-lfw-people">Examples using <code class="docutils literal"><span class="pre">sklearn.datasets.fetch_lfw_people</span></code></a></li>
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<div class="section" id="sklearn-datasets-fetch-lfw-people">
<h1><a class="reference internal" href="../classes.html#module-sklearn.datasets" title="sklearn.datasets"><code class="xref py py-mod docutils literal"><span class="pre">sklearn.datasets</span></code></a>.fetch_lfw_people<a class="headerlink" href="#sklearn-datasets-fetch-lfw-people" title="Permalink to this headline">¶</a></h1>
<dl class="function">
<dt id="sklearn.datasets.fetch_lfw_people">
<code class="descclassname">sklearn.datasets.</code><code class="descname">fetch_lfw_people</code><span class="sig-paren">(</span><em>data_home=None</em>, <em>funneled=True</em>, <em>resize=0.5</em>, <em>min_faces_per_person=0</em>, <em>color=False</em>, <em>slice_=(slice(70</em>, <em>195</em>, <em>None)</em>, <em>slice(78</em>, <em>172</em>, <em>None))</em>, <em>download_if_missing=True</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/51a765a/sklearn/datasets/lfw.py#L226"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.datasets.fetch_lfw_people" title="Permalink to this definition">¶</a></dt>
<dd><p>Loader for the Labeled Faces in the Wild (LFW) people dataset</p>
<p>This dataset is a collection of JPEG pictures of famous people
collected on the internet, all details are available on the
official website:</p>
<blockquote>
<div><a class="reference external" href="http://vis-www.cs.umass.edu/lfw/">http://vis-www.cs.umass.edu/lfw/</a></div></blockquote>
<p>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.</p>
<p>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).</p>
<p>The original images are 250 x 250 pixels, but the default slice and resize
arguments reduce them to 62 x 74.</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><p class="first"><strong>data_home</strong> : optional, default: None</p>
<blockquote>
<div><p>Specify another download and cache folder for the datasets. By default
all scikit learn data is stored in ‘~/scikit_learn_data’ subfolders.</p>
</div></blockquote>
<p><strong>funneled</strong> : boolean, optional, default: True</p>
<blockquote>
<div><p>Download and use the funneled variant of the dataset.</p>
</div></blockquote>
<p><strong>resize</strong> : float, optional, default 0.5</p>
<blockquote>
<div><p>Ratio used to resize the each face picture.</p>
</div></blockquote>
<p><strong>min_faces_per_person</strong> : int, optional, default None</p>
<blockquote>
<div><p>The extracted dataset will only retain pictures of people that have at
least <cite>min_faces_per_person</cite> different pictures.</p>
</div></blockquote>
<p><strong>color</strong> : boolean, optional, default False</p>
<blockquote>
<div><p>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 than the shape with color = False.</p>
</div></blockquote>
<p><strong>slice_</strong> : optional</p>
<blockquote>
<div><p>Provide a custom 2D slice (height, width) to extract the
‘interesting’ part of the jpeg files and avoid use statistical
correlation from the background</p>
</div></blockquote>
<p><strong>download_if_missing</strong> : optional, True by default</p>
<blockquote>
<div><p>If False, raise a IOError if the data is not locally available
instead of trying to download the data from the source site.</p>
</div></blockquote>
</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>dataset</strong> : dict-like object with the following attributes:</p>
<p><strong>dataset.data</strong> : numpy array of shape (13233, 2914)</p>
<blockquote>
<div><p>Each row corresponds to a ravelled face image of original size 62 x 47
pixels. Changing the <code class="docutils literal"><span class="pre">slice_</span></code> or resize parameters will change the shape
of the output.</p>
</div></blockquote>
<p><strong>dataset.images</strong> : numpy array of shape (13233, 62, 47)</p>
<blockquote>
<div><p>Each row is a face image corresponding to one of the 5749 people in
the dataset. Changing the <code class="docutils literal"><span class="pre">slice_</span></code> or resize parameters will change the shape
of the output.</p>
</div></blockquote>
<p><strong>dataset.target</strong> : numpy array of shape (13233,)</p>
<blockquote>
<div><p>Labels associated to each face image. Those labels range from 0-5748
and correspond to the person IDs.</p>
</div></blockquote>
<p><strong>dataset.DESCR</strong> : string</p>
<blockquote class="last">
<div><p>Description of the Labeled Faces in the Wild (LFW) dataset.</p>
</div></blockquote>
</td>
</tr>
</tbody>
</table>
</dd></dl>
<div class="section" id="examples-using-sklearn-datasets-fetch-lfw-people">
<h2>Examples using <code class="docutils literal"><span class="pre">sklearn.datasets.fetch_lfw_people</span></code><a class="headerlink" href="#examples-using-sklearn-datasets-fetch-lfw-people" title="Permalink to this headline">¶</a></h2>
<div class="thumbnailContainer" tooltip="The dataset used in this example is a preprocessed excerpt of the "Labeled Faces in the Wild", ..."><div class="figure" id="id1">
<a class="reference external image-reference" href="./../../auto_examples/applications/face_recognition.html"><img alt="../../_images/face_recognition1.png" src="../../_images/face_recognition1.png" /></a>
<p class="caption"><span class="caption-text"><a class="reference internal" href="../../auto_examples/applications/face_recognition.html#example-applications-face-recognition-py"><span>Faces recognition example using eigenfaces and SVMs</span></a></span></p>
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