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diff --git a/site/public/datasets/lfw/what/index.html b/site/public/datasets/lfw/what/index.html new file mode 100644 index 00000000..ceafb35a --- /dev/null +++ b/site/public/datasets/lfw/what/index.html @@ -0,0 +1,142 @@ +<!doctype html> +<html> +<head> + <title>MegaPixels</title> + <meta charset="utf-8" /> + <meta name="author" content="Adam Harvey" /> + <meta name="description" content="LFW: Labeled Faces in The Wild" /> + <meta name="referrer" content="no-referrer" /> + <meta name="viewport" content="width=device-width, initial-scale=1.0, user-scalable=yes" /> + <link rel='stylesheet' href='/assets/css/fonts.css' /> + <link rel='stylesheet' href='/assets/css/css.css' /> +</head> +<body> + <header> + <a class='slogan' href="/"> + <div class='logo'></div> + <div class='site_name'>MegaPixels</div> + <span class='sub'>The Darkside of Datasets</span> + </a> + <div class='links'> + <a href="/search/">Face Search</a> + <a href="/datasets/">Datasets</a> + <a href="/research/">Research</a> + <a href="/about/">About</a> + </div> + </header> + <div class="content"> + + <section><h1>Labeled Faces in The Wild</h1> +<ul> +<li>Created 2007 (auto)</li> +<li>Images 13,233 (auto)</li> +<li>People 5,749 (auto)</li> +<li>Created From Yahoo News images (auto)</li> +<li>Analyzed and searchable (auto)</li> +</ul> +<p><em>Labeled Faces in The Wild</em> is amongst the most widely used facial recognition training datasets in the world and is the first facial recognition dataset [^lfw_names_faces] of its kind to be created entirely from Internet photos. It includes 13,233 images of 5,749 people that appeared on Yahoo News between 2002 - 2004.</p> +</section><section class='images'><div class='image'><img src='https://nyc3.digitaloceanspaces.com/megapixels/v1/datasets/lfw/assets/lfw_grid_preview.jpg' alt='Eight out of 5,749 people in the Labeled Faces in the Wild dataset. The face recognition training dataset is created entirely from photos downloaded from the Internet.'><div class='caption'>Eight out of 5,749 people in the Labeled Faces in the Wild dataset. The face recognition training dataset is created entirely from photos downloaded from the Internet.</div></div></section><section><h2>INTRO</h2> +<p>It began in 2002. Researchers at University of Massachusetts Amherst were developing algorithms for facial recognition and they needed more data. Between 2002-2004 they scraped Yahoo News for images of public figures. Two years later they cleaned up the dataset and repackaged it as Labeled Faces in the Wild (LFW).</p> +<p>Since then the LFW dataset has become one of the most widely used datasets used for evaluating face recognition algorithms. The associated research paper “Labeled Faces in the Wild: A Database for Studying Face Recognition in Unconstrained Environments” has been cited 996 times reaching 45 different countries throughout the world.</p> +<p>The faces come from news stories and are mostly celebrities from the entertainment industry, politicians, and villains. It’s a sampling of current affairs and breaking news that has come to pass. The images, detached from their original context now server a new purpose: to train, evaluate, and improve facial recognition.</p> +<p>As the most widely used facial recognition dataset, it can be said that each individual in LFW has, in a small way, contributed to the current state of the art in facial recognition surveillance. John Cusack, Julianne Moore, Barry Bonds, Osama bin Laden, and even Moby are amongst these biometric pillars, exemplar faces provided the visual dimensions of a new computer vision future.</p> +</section><section class='images'><div class='image'><img src='https://nyc3.digitaloceanspaces.com/megapixels/v1/datasets/lfw/assets/lfw_a_to_c.jpg' alt='From Aaron Eckhart to Zydrunas Ilgauskas. A small sampling of the LFW dataset'><div class='caption'>From Aaron Eckhart to Zydrunas Ilgauskas. A small sampling of the LFW dataset</div></div></section><section><p>In addition to commercial use as an evaluation tool, all of the faces in LFW dataset are prepackaged into a popular machine learning code framework called scikit-learn.</p> +<h2>Usage</h2> +<pre><code class="lang-python">#!/usr/bin/python +from matplotlib import plt +from sklearn.datasets import fetch_lfw_people +lfw_people = fetch_lfw_people() +lfw_person = lfw_people[0] +plt.imshow(lfw_person) +</code></pre> +<h2>Commercial Use</h2> +<p>The LFW dataset is used by numerous companies for benchmarking algorithms and in some cases training. According to the benchmarking results page [^lfw_results] provided by the authors, over 2 dozen companies have contributed their benchmark results</p> +<pre><code>load file: lfw_commercial_use.csv +name_display,company_url,example_url,country,description +</code></pre> +<table> +<thead><tr> +<th style="text-align:left">Company</th> +<th style="text-align:left">Country</th> +<th style="text-align:left">Industries</th> +</tr> +</thead> +<tbody> +<tr> +<td style="text-align:left"><a href="http://www.aratek.co">Aratek</a></td> +<td style="text-align:left">China</td> +<td style="text-align:left">Biometric sensors for telecom, civil identification, finance, education, POS, and transportation</td> +</tr> +<tr> +<td style="text-align:left"><a href="http://www.aratek.co">Aratek</a></td> +<td style="text-align:left">China</td> +<td style="text-align:left">Biometric sensors for telecom, civil identification, finance, education, POS, and transportation</td> +</tr> +<tr> +<td style="text-align:left"><a href="http://www.aratek.co">Aratek</a></td> +<td style="text-align:left">China</td> +<td style="text-align:left">Biometric sensors for telecom, civil identification, finance, education, POS, and transportation</td> +</tr> +</tbody> +</table> +<p>Add 2-4 screenshots of companies mentioning LFW here</p> +</section><section class='images'><div class='image'><img src='https://nyc3.digitaloceanspaces.com/megapixels/v1/datasets/lfw/assets/lfw_screenshot_01.png' alt='ReadSense'><div class='caption'>ReadSense</div></div></section><section><p>In benchmarking, companies use a dataset to evaluate their algorithms which are typically trained on other data. After training, researchers will use LFW as a benchmark to compare results with other algorithms.</p> +<p>For example, Baidu (est. net worth $13B) uses LFW to report results for their "Targeting Ultimate Accuracy: Face Recognition via Deep Embedding". According to the three Baidu researchers who produced the paper:</p> +<blockquote><p>LFW has been the most popular evaluation benchmark for face recognition, and played a very important role in facilitating the face recognition society to improve algorithm. <sup class="footnote-ref" id="fnref-baidu_lfw"><a href="#fn-baidu_lfw">1</a></sup>.</p> +</blockquote> +<h2>Citations</h2> +<table> +<thead><tr> +<th style="text-align:left">Title</th> +<th style="text-align:left">Organization</th> +<th style="text-align:left">Country</th> +<th style="text-align:left">Type</th> +</tr> +</thead> +<tbody> +<tr> +<td style="text-align:left">3D-aided face recognition from videos</td> +<td style="text-align:left">University of Lyon</td> +<td style="text-align:left">France</td> +<td style="text-align:left">edu</td> +</tr> +<tr> +<td style="text-align:left">A Community Detection Approach to Cleaning Extremely Large Face Database</td> +<td style="text-align:left">National University of Defense Technology, China</td> +<td style="text-align:left">China</td> +<td style="text-align:left">edu</td> +</tr> +</tbody> +</table> +<h2>Conclusion</h2> +<p>The LFW face recognition training and evaluation dataset is a historically important face dataset as it was the first popular dataset to be created entirely from Internet images, paving the way for a global trend towards downloading anyone’s face from the Internet and adding it to a dataset. As will be evident with other datasets, LFW’s approach has now become the norm.</p> +<p>For all the 5,000 people in this datasets, their face is forever a part of facial recognition history. It would be impossible to remove anyone from the dataset because it is so ubiquitous. For their rest of the lives and forever after, these 5,000 people will continue to be used for training facial recognition surveillance.</p> +<h2>Notes</h2> +<p>According to BiometricUpdate.com<sup class="footnote-ref" id="fnref-biometric_update_lfw"><a href="#fn-biometric_update_lfw">2</a></sup>, LFW is "the most widely used evaluation set in the field of facial recognition, LFW attracts a few dozen teams from around the globe including Google, Facebook, Microsoft Research Asia, Baidu, Tencent, SenseTime, Face++ and Chinese University of Hong Kong."</p> +<div class="footnotes"> +<hr> +<ol><li id="fn-baidu_lfw"><p>"Chinese tourist town uses face recognition as an entry pass". New Scientist. November 17, 2016. <a href="https://www.newscientist.com/article/2113176-chinese-tourist-town-uses-face-recognition-as-an-entry-pass/">https://www.newscientist.com/article/2113176-chinese-tourist-town-uses-face-recognition-as-an-entry-pass/</a><a href="#fnref-baidu_lfw" class="footnote">↩</a></p></li> +<li id="fn-biometric_update_lfw"><p>"PING AN Tech facial recognition receives high score in latest LFW test results". <a href="https://www.biometricupdate.com/201702/ping-an-tech-facial-recognition-receives-high-score-in-latest-lfw-test-results">https://www.biometricupdate.com/201702/ping-an-tech-facial-recognition-receives-high-score-in-latest-lfw-test-results</a><a href="#fnref-biometric_update_lfw" class="footnote">↩</a></p></li> +</ol> +</div> +</section> + + </div> + <footer> + <div> + <a href="/">MegaPixels.cc</a> + <a href="/about/disclaimer/">Disclaimer</a> + <a href="/about/terms/">Terms of Use</a> + <a href="/about/privacy/">Privacy</a> + <a href="/about/">About</a> + <a href="/about/team/">Team</a> + </div> + <div> + MegaPixels ©2017-19 Adam R. 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