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diff --git a/site/public/datasets/lfw/index.html b/site/public/datasets/lfw/index.html index a6226720..5b5e58f3 100644 --- a/site/public/datasets/lfw/index.html +++ b/site/public/datasets/lfw/index.html @@ -4,7 +4,7 @@ <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="description" content="Labeled Faces in The Wild (LFW) is a database of face photographs designed for studying the problem of unconstrained face recognition." /> <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' /> @@ -27,54 +27,42 @@ </header> <div class="content"> - <section><h1>Labeled Faces in the Wild</h1> -</section><section><div class='meta'><div><div class='gray'>Created</div><div>2007</div></div><div><div class='gray'>Images</div><div>13,233</div></div><div><div class='gray'>People</div><div>5,749</div></div><div><div class='gray'>Created From</div><div>Yahoo News images</div></div><div><div class='gray'>Search available</div><div>Searchable</div></div></div></section><section class='applet_container'><div class='applet' data-payload='{"command": "face_search"}'></div></section><section class='applet_container'><div class='applet' data-payload='{"command": "name_search"}'></div></section><section class='fullwidth'><div class='image'><img src='https://nyc3.digitaloceanspaces.com/megapixels/v1/datasets/lfw/assets/lfw_feature.jpg' alt='Eighteen of the 5,749 people in the Labeled Faces in the Wild Dataset. The most widely used face dataset for benchmarking commercial face recognition algorithms.'><div class='caption'>Eighteen of the 5,749 people in the Labeled Faces in the Wild Dataset. The most widely used face dataset for benchmarking commercial face recognition algorithms.</div></div></section><section><h3>Intro</h3> -<p>Labeled Faces in The Wild (LFW) is among the most widely used facial recognition training datasets in the world and is the first of its kind to be created entirely from images posted online. The LFW dataset includes 13,233 images of 5,749 people that were collected between 2002-2004. Use the tools below to check if you were included in this dataset or scroll down to read the analysis.</p> -<p>Three paragraphs describing the LFW dataset in a format that can be easily replicated for the other datasets. Nothing too custom. An analysis of the initial research papers with context relative to all the other dataset papers.</p> -</section><section class='fullwidth'><div class='image'><img src='https://nyc3.digitaloceanspaces.com/megapixels/v1/datasets/lfw/assets/lfw_montage_everyone_nocrop_1920.jpg' alt=' From George W. Bush to Jamie Lee Curtis: all 5,749 people in the LFW Dataset sorted from most to least images collected.'><div class='caption'> From George W. Bush to Jamie Lee Curtis: all 5,749 people in the LFW Dataset sorted from most to least images collected.</div></div></section><section><h3>LFW by the Numbers</h3> + <section class='intro_section' style='background-image: url(https://nyc3.digitaloceanspaces.com/megapixels/v1/datasets/lfw/assets/background.jpg)'><div class='inner'><div class='hero_desc'><span><span style='color: #ff0000'>Labeled Faces in The Wild</span> (LFW) is a database of face photographs designed for studying the problem of unconstrained face recognition.</span></div><div class='hero_subdesc'><span>It includes 13,456 images of 4,432 people’s images copied from the Internet during 2002-2004. +</span></div></div></section><section><div class='image'><div class='caption'>A few of the 5,749 people in the Labeled Faces in the Wild Dataset. The most widely used face dataset for benchmarking commercial face recognition algorithms.</div></div></section><section><div class='right-sidebar'><h3>Statistics</h3> +<div class='meta'><div><div class='gray'>Years</div><div>2002-2004</div></div><div><div class='gray'>Images</div><div>13,233</div></div><div><div class='gray'>Identities</div><div>5,749</div></div><div><div class='gray'>Origin</div><div>Yahoo News Images</div></div><div><div class='gray'>Funding</div><div>(Possibly, partially CIA)</div></div></div><h3>INSIGHTS</h3> <ul> -<li>Was first published in 2007</li> -<li>Developed out of a prior dataset from Berkely called "Faces in the Wild" or "Names and Faces" [^lfw_original_paper]</li> -<li>Includes 13,233 images and 5,749 different people [^lfw_website]</li> -<li>There are about 3 men for every 1 woman (4,277 men and 1,472 women)[^lfw_website]</li> -<li>The person with the most images is George W. Bush with 530</li> -<li>Most people (70%) in the dataset have only 1 image</li> -<li>Thre are 1,680 people in the dataset with 2 or more images [^lfw_website]</li> -<li>Two out of 4 of the original authors received funding from the Office of Director of National Intelligence and IARPA for their 2016 LFW survey follow up report </li> -<li>The LFW dataset includes over 500 actors, 30 models, 10 presidents, 24 football players, 124 basketball players, 11 kings, and 2 queens</li> -<li>In all the LFW publications provided by the authors the words "ethics", "consent", and "privacy" appear 0 times [^lfw_original_paper], [^lfw_survey], [^lfw_tech_report] , [^lfw_website]</li> +<li>There are about 3 men for every 1 woman (4,277 men and 1,472 women) in the LFW dataset<a class="footnote_shim" name="[^lfw_www]_1"> </a><a href="#[^lfw_www]" class="footnote" title="Footnote 1">1</a></li> +<li>The person with the most images is <a href="http://vis-www.cs.umass.edu/lfw/person/George_W_Bush_comp.html">George W. Bush</a> with 530</li> +<li>There are about 3 George W. Bush's for every 1 <a href="http://vis-www.cs.umass.edu/lfw/person/Tony_Blair.html">Tony Blair</a></li> +<li>The LFW dataset includes over 500 actors, 30 models, 10 presidents, 124 basketball players, 24 football players, 11 kings, 7 queens, and 1 <a href="http://vis-www.cs.umass.edu/lfw/person/Moby.html">Moby</a></li> +<li>In all 3 of the LFW publications [^lfw_original_paper], [^lfw_survey], [^lfw_tech_report] the words "ethics", "consent", and "privacy" appear 0 times</li> <li>The word "future" appears 71 times</li> </ul> -<h3>Facts</h3> +</div><h2>Labeled Faces in the Wild</h2> +<p><em>Labeled Faces in The Wild</em> (LFW) is "a database of face photographs designed for studying the problem of unconstrained face recognition<a class="footnote_shim" name="[^lfw_www]_2"> </a><a href="#[^lfw_www]" class="footnote" title="Footnote 1">1</a>. It is used to evaluate and improve the performance of facial recognition algorithms in academic, commercial, and government research. According to BiometricUpdate.com<a class="footnote_shim" name="[^lfw_pingan]_1"> </a><a href="#[^lfw_pingan]" class="footnote" title="Footnote 3">3</a>, 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> +<p>The LFW dataset includes 13,233 images of 5,749 people that were collected between 2002-2004. LFW is a subset of <em>Names of Faces</em> and is part of the first facial recognition training dataset created entirely from images appearing on the Internet. The people appearing in LFW are...</p> +<p>The <em>Names and Faces</em> dataset was the first face recognition dataset created entire from online photos. However, <em>Names and Faces</em> and <em>LFW</em> are not the first face recognition dataset created entirely "in the wild". That title belongs to the <a href="/datasets/ucd_faces/">UCD dataset</a>. Images obtained "in the wild" means using an image without explicit consent or awareness from the subject or photographer.</p> +<h3>Biometric Trade Routes</h3> +<p>To understand how this dataset has been used, its citations have been geocoded to show an approximate geographic digital trade route of the biometric data. Lines indicate an organization (education, commercial, or governmental) that has cited the LFW dataset in their research. Data is compiled from <a href="https://www.semanticscholar.org">Semantic Scholar</a>.</p> +</section><section class='applet_container'><div class='applet' data-payload='{"command": "map"}'></div></section><section><h3>Synthetic Faces</h3> +<p>To visualize the types of photos in the dataset without explicitly publishing individual's identities a generative adversarial network (GAN) was trained on the entire dataset. The images in this video show a neural network learning the visual latent space and then interpolating between archetypical identities within the LFW dataset.</p> +</section><section class='fullwidth'><div class='image'><img src='https://nyc3.digitaloceanspaces.com/megapixels/v1/datasets/lfw/assets/lfw_synthetic.jpg' alt=''></div></section><section><h3>Citations</h3> +<p>Browse or download the geocoded citation data collected for the LFW dataset.</p> +</section><section class='applet_container'><div class='applet' data-payload='{"command": "citations"}'></div></section><section><h3>Additional Information</h3> +<p>(tweet-sized snippets go here)</p> <ul> -<li>Was created for the purpose of improving "unconstrained face recognition" [^lfw_original_paper]</li> -<li>All images in LFW were obtained "in the wild" meaning without any consent from the subject or from the photographer</li> -<li>The faces were detected using the Viola-Jones haarcascade face detector [^lfw_website] [^lfw_survey]</li> -<li>Is considered the "most popular benchmark for face recognition" [^lfw_baidu]</li> -<li>Is "the most widely used evaluation set in the field of facial recognition" [^lfw_pingan]</li> -<li><p>Is used by several of the largest tech companies in the world including "Google, Facebook, Microsoft Research Asia, Baidu, Tencent, SenseTime, Face++ and Chinese University of Hong Kong." [^lfw_pingan]</p> -</li> -<li><p>All images were copied from Yahoo News between 2002 - 2004 [^lfw_original_paper]</p> -</li> -<li>SenseTime, who has relied on LFW for benchmarking their facial recognition performance, is the leading provider of surveillance to the Chinese Government</li> +<li>The LFW dataset is considered the "most popular benchmark for face recognition" <a class="footnote_shim" name="[^lfw_baidu]_1"> </a><a href="#[^lfw_baidu]" class="footnote" title="Footnote 2">2</a></li> +<li>The LFW dataset is "the most widely used evaluation set in the field of facial recognition" <a class="footnote_shim" name="[^lfw_pingan]_2"> </a><a href="#[^lfw_pingan]" class="footnote" title="Footnote 3">3</a></li> +<li>All images in LFW dataset were obtained "in the wild" meaning without any consent from the subject or from the photographer</li> +<li>The faces in the LFW dataset were detected using the Viola-Jones haarcascade face detector [^lfw_website] [^lfw-survey]</li> +<li>The LFW dataset is used by several of the largest tech companies in the world including "Google, Facebook, Microsoft Research Asia, Baidu, Tencent, SenseTime, Face++ and Chinese University of Hong Kong." <a class="footnote_shim" name="[^lfw_pingan]_3"> </a><a href="#[^lfw_pingan]" class="footnote" title="Footnote 3">3</a></li> +<li>All images in the LFW dataset were copied from Yahoo News between 2002 - 2004</li> +<li>In 2014, two of the four original authors of the LFW dataset received funding from IARPA and ODNI for their followup paper <a href="https://www.semanticscholar.org/paper/Labeled-Faces-in-the-Wild-%3A-Updates-and-New-Huang-Learned-Miller/2d3482dcff69c7417c7b933f22de606a0e8e42d4">Labeled Faces in the Wild: Updates and New Reporting Procedures</a> via IARPA contract number 2014-14071600010</li> +<li>The dataset includes 2 images of <a href="http://vis-www.cs.umass.edu/lfw/person/George_Tenet.html">George Tenet</a>, the former Director of Central Intelligence (DCI) for the Central Intelligence Agency whose facial biometrics were eventually used to help train facial recognition software in China and Russia</li> </ul> </section><section class='images'><div class='image'><img src='https://nyc3.digitaloceanspaces.com/megapixels/v1/datasets/lfw/assets/lfw_montage_top1_640.jpg' alt=' former President George W. Bush'><div class='caption'> former President George W. Bush</div></div> -<div class='image'><img src='https://nyc3.digitaloceanspaces.com/megapixels/v1/datasets/lfw/assets/lfw_montage_top2_4_640.jpg' alt=' Colin Powell (236), Tony Blair (144), and Donald Rumsfeld (121)'><div class='caption'> Colin Powell (236), Tony Blair (144), and Donald Rumsfeld (121)</div></div></section><section><h3>People and Companies using the LFW Dataset</h3> -<p>This section describes who is using the dataset and for what purposes. It should include specific examples of people or companies with citations and screenshots. This section is followed up by the graph, the map, and then the supplementary material.</p> -<p>The LFW dataset is used by numerous companies for <a href="about/glossary#benchmarking">benchmarking</a> algorithms and in some cases <a href="about/glossary#training">training</a>. According to the benchmarking results page [^lfw_results] provided by the authors, over 2 dozen companies have contributed their benchmark results.</p> -<p>According to BiometricUpdate.com [^lfw_pingan], 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> -<p>According to researchers at the Baidu Research – Institute of Deep Learning "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. [^lfw_baidu]."</p> -<p>In addition to commercial use as an evaluation tool, alll of the faces in LFW dataset are prepackaged into a popular machine learning code framework called scikit-learn.</p> -</section><section class='images'><div class='image'><img src='https://nyc3.digitaloceanspaces.com/megapixels/v1/datasets/lfw/assets/lfw_screenshot_01.jpg' alt=' "PING AN Tech facial recognition receives high score in latest LFW test results"'><div class='caption'> "PING AN Tech facial recognition receives high score in latest LFW test results"</div></div> -<div class='image'><img src='https://nyc3.digitaloceanspaces.com/megapixels/v1/datasets/lfw/assets/lfw_screenshot_02.jpg' alt=' "Face Recognition Performance in LFW benchmark"'><div class='caption'> "Face Recognition Performance in LFW benchmark"</div></div> -<div class='image'><img src='https://nyc3.digitaloceanspaces.com/megapixels/v1/datasets/lfw/assets/lfw_screenshot_03.jpg' alt=' "The 1st place in face verification challenge, LFW"'><div class='caption'> "The 1st place in face verification challenge, LFW"</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> -<h3>Citations</h3> -<p>Overall, LFW has at least 116 citations from 11 countries.</p> -</section><section class='applet_container'><div class='applet' data-payload='{"command": "map"}'></div></section><section class='applet_container'><div class='applet' data-payload='{"command": "citations"}'></div></section><section><h3>Conclusion</h3> -<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>Code</h2> +<div class='image'><img src='https://nyc3.digitaloceanspaces.com/megapixels/v1/datasets/lfw/assets/lfw_montage_top2_4_640.jpg' alt=' Colin Powell (236), Tony Blair (144), and Donald Rumsfeld (121)'><div class='caption'> Colin Powell (236), Tony Blair (144), and Donald Rumsfeld (121)</div></div></section><section class='images'><div class='image'><img src='https://nyc3.digitaloceanspaces.com/megapixels/v1/datasets/lfw/assets/lfw_montage_all_crop.jpg' alt='All 5,379 faces in the Labeled Faces in The Wild Dataset'><div class='caption'>All 5,379 faces in the Labeled Faces in The Wild Dataset</div></div></section><section><h2>Code</h2> +<p>The LFW dataset is so widely used that a popular code library called Sci-Kit Learn includes a function called <code>fetch_lfw_people</code> to download the faces in the LFW dataset.</p> </section><section><pre><code class="lang-python">#!/usr/bin/python import numpy as np @@ -87,31 +75,29 @@ lfw_people = fetch_lfw_people(min_faces_per_person=1, resize=1, color=True, funn # introspect dataset n_samples, h, w, c = lfw_people.images.shape -print('{:,} images at {}x{}'.format(n_samples, w, h)) +print(f'{n_samples:,} images at {w}x{h} pixels') cols, rows = (176, 76) n_ims = cols * rows # build montages im_scale = 0.5 -ims = lfw_people.images[:n_ims -montages = imutils.build_montages(ims, (int(w*im_scale, int(h*im_scale)), (cols, rows)) +ims = lfw_people.images[:n_ims] +montages = imutils.build_montages(ims, (int(w * im_scale, int(h * im_scale)), (cols, rows)) montage = montages[0] # save full montage image imageio.imwrite('lfw_montage_full.png', montage) # make a smaller version -montage_960 = imutils.resize(montage, width=960) -imageio.imwrite('lfw_montage_960.jpg', montage_960) +montage = imutils.resize(montage, width=960) +imageio.imwrite('lfw_montage_960.jpg', montage) </code></pre> -</section><section><h2>Disclaimer</h2> -<p>MegaPixels is an educational art project designed to encourage discourse about facial recognition datasets. Any ethical or legal issues should be directed to the researcher's parent organizations. Except where necessary for contact or clarity, the names of researchers have been subsituted by their parent organization. In no way does this project aim to villify researchers who produced the datasets.</p> -<p>Read more about <a href="about/code-of-conduct">MegaPixels Code of Conduct</a></p> -<div class="footnotes"> -<hr> -<ol></ol> -</div> -</section> +</section><section><h3>Supplementary Material</h3> +</section><section class='applet_container'><div class='applet' data-payload='{"command": "load_file assets/lfw_commercial_use.csv", "fields": ["name_display, company_url, example_url, country, description"]}'></div></section><section><p>Text and graphics ©Adam Harvey / megapixels.cc</p> +</section><section><ul class="footnotes"><li><a name="[^lfw_www]" class="footnote_shim"></a><span class="backlinks"><a href="#[^lfw_www]_1">a</a><a href="#[^lfw_www]_2">b</a></span><p><a href="http://vis-www.cs.umass.edu/lfw/results.html">http://vis-www.cs.umass.edu/lfw/results.html</a></p> +</li><li><a name="[^lfw_baidu]" class="footnote_shim"></a><span class="backlinks"><a href="#[^lfw_baidu]_1">a</a></span><p>Jingtuo Liu, Yafeng Deng, Tao Bai, Zhengping Wei, Chang Huang. Targeting Ultimate Accuracy: Face Recognition via Deep Embedding. <a href="https://arxiv.org/abs/1506.07310">https://arxiv.org/abs/1506.07310</a></p> +</li><li><a name="[^lfw_pingan]" class="footnote_shim"></a><span class="backlinks"><a href="#[^lfw_pingan]_1">a</a><a href="#[^lfw_pingan]_2">b</a><a href="#[^lfw_pingan]_3">c</a></span><p>Lee, Justin. "PING AN Tech facial recognition receives high score in latest LFW test results". BiometricUpdate.com. Feb 13, 2017. <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></p> +</li></ul></section> </div> <footer> |