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<title>MegaPixels: IJB-C</title>
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<div class='site_name'>MegaPixels</div>
<div class='page_name'>IJB-C</div>
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<a href="/about/">About</a>
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<section class='intro_section' style='background-image: url(https://nyc3.digitaloceanspaces.com/megapixels/v1/datasets/ijb_c/assets/background.jpg)'></section><section><h2>IARPA Janus Benchmark C (IJB-C)</h2>
</section><section><div class='right-sidebar'><div class='meta'>
<div class='gray'>Published</div>
<div>2017</div>
</div><div class='meta'>
<div class='gray'>Images</div>
<div>21,294 </div>
</div><div class='meta'>
<div class='gray'>Videos</div>
<div>11,799 </div>
</div><div class='meta'>
<div class='gray'>Identities</div>
<div>3,531 </div>
</div><div class='meta'>
<div class='gray'>Purpose</div>
<div>Face recognition</div>
</div><div class='meta'>
<div class='gray'>Website</div>
<div><a href='https://www.nist.gov/programs-projects/face-challenges' target='_blank' rel='nofollow noopener'>nist.gov</a></div>
</div></div><p>[ page under development ]</p>
<p>The IARPA Janus Benchmark C (IJB–C) is a dataset of web images used for face recognition research and development. The IJB–C dataset contains 3,531 people from 21,294 images and 3,531 videos. The list of 3,531 names are activists, artists, journalists, foreign politicians, and public speakers.</p>
<p>Key Findings:</p>
<ul>
<li>metadata annotations were created using crowd annotations on Mechanical Turk</li>
<li>The dataset was creatd Nobilis</li>
<li>made for intelligence analysts</li>
<li>improve performance of face recognition tools</li>
<li>by fusing the rich spatial, temporal, and contextual information available from the multiple views captured by today’s "media in the wild"</li>
</ul>
<p>The dataset includes Creative Commons images</p>
<p>The name list includes</p>
<ul>
<li>2 videos from CCC<ul>
<li>yq6ZC-YLHZA.png<ul>
<li>Katharina Nocun: Deine Rechte sind in diesen Freihandelsabkommen nicht verfügbar</li>
</ul>
</li>
<li>fF2MxkDzlVg<ul>
<li>Jillian York: "Technology companies now hold an unprecedented ability to shape the world around us by limiting our ability to access certain content and by crafting proprietary algorithm that bring us our daily streams of content. Matthew Stender, Jillian C. York"</li>
</ul>
</li>
<li>Maya Zankoul. She's an old friend, a Lebanese web designer who's put out a couple of books locally and has a Wikipedia page, probably created by a Lebanese Wikipedia editor die-hard. Not famous. How on earth?</li>
<li>Melissa Gira Grant (also a journalist)</li>
<li>Nadezhda Tolokinnikova (Pussy Riot)</li>
<li>Derrick Ashong (activist and journalist)</li>
<li>Michael Anti</li>
<li>Lina Ben Mhenni</li>
<li>Manal al-Sharif</li>
<li>Juan Carlos de Martin (not an activist but not really famous either!)</li>
<li>Anita Sarkeesian</li>
<li>Amal Clooney (lawyer)</li>
<li>Anil Dash (startup guy)</li>
<li>Bruno Latour (philosopher)</li>
<li>Dan Gillmor (tech journalist)</li>
<li>Eben Upton (founder of raspberry pi)</li>
<li>Evgeny Morozov</li>
<li>Gabriella Coleman</li>
<li>Maria Popova</li>
<li>Molly Crabapple</li>
<li>Paola Antonelli</li>
<li>Seymour Hersh</li>
<li>Ta-Nehisi Coates</li>
</ul>
</li>
</ul>
<p>The first 777 are non-alphabetical. From 777-3531 is alphabetical</p>
</section><section class='images'><div class='image'><img src='https://nyc3.digitaloceanspaces.com/megapixels/v1/datasets/ijb_c/assets/ijb_c_montage.jpg' alt=' A visualization of the IJB-C dataset'><div class='caption'> A visualization of the IJB-C dataset</div></div></section><section><h2>Research notes</h2>
<p>From original papers: <a href="https://noblis.org/wp-content/uploads/2018/03/icb2018.pdf">https://noblis.org/wp-content/uploads/2018/03/icb2018.pdf</a></p>
<p>Collection for the dataset began by identifying CreativeCommons subject videos, which are often more scarce than Creative Commons subject images. Search terms that re-sulted in large quantities of person-centric videos (e.g. “in-terview”) were generated and translated into numerous lan-guages including Arabic, Korean, Swahili, and Hindi to in-crease diversity of the subject pool. Certain YouTube userswho upload well-labeled, person-centric videos, such as the World Economic Forum and the International University Sports Federation were also identified. Titles of videos per-taining to these search terms and usernames were scrapedusing the YouTube Data API and translated into English us-ing the Yandex Translate API4. Pattern matching was per-formed to extract potential names of subjects from the trans-lated titles, and these names were searched using the Wiki-data API to verify the subject’s existence and status as a public figure, and to check for Wikimedia Commons im-agery. Age, gender, and geographic region were collectedusing the Wikipedia API.Using the candidate subject names, Creative Commonsimages were scraped from Google and Wikimedia Com-mons, and Creative Commons videos were scraped fromYouTube. After images and videos of the candidate subjectwere identified, AMT Workers were tasked with validat-ing the subject’s presence throughout the video. The AMTWorkers marked segments of the video in which the subjectwas present, and key frames</p>
<p>IARPA funds Italian researcher <a href="https://www.micc.unifi.it/projects/glaivejanus/">https://www.micc.unifi.it/projects/glaivejanus/</a></p>
</section><section>
<h3>Who used IJB-C?</h3>
<p>
This bar chart presents a ranking of the top countries where dataset citations originated. Mouse over individual columns to see yearly totals. These charts show at most the top 10 countries.
</p>
</section>
<section class="applet_container">
<!-- <div style="position: absolute;top: 0px;right: -55px;width: 180px;font-size: 14px;">Labeled Faces in the Wild Dataset<br><span class="numc" style="font-size: 11px;">20 citations</span>
</div> -->
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</section>
<section class="applet_container">
<div class="applet" data-payload="{"command": "piechart"}"></div>
</section>
<section>
<h3>Information Supply Chain</h3>
<p>
To help understand how IJB-C has been used around the world by commercial, military, and academic organizations; existing publicly available research citing IARPA Janus Benchmark C was collected, verified, and geocoded to show how AI training data has proliferated around the world. Click on the markers to reveal research projects at that location.
</p>
</section>
<section class="applet_container fullwidth">
<div class="applet" data-payload="{"command": "map"}"></div>
</section>
<div class="caption">
<ul class="map-legend">
<li class="edu">Academic</li>
<li class="com">Commercial</li>
<li class="gov">Military / Government</li>
</ul>
<div class="source">Citation data is collected using SemanticScholar.org then dataset usage verified and geolocated. Citations are used to provide overview of how and where images were used.</div>
</div>
<section class="applet_container">
<h3>Dataset Citations</h3>
<p>
The dataset citations used in the visualizations were collected from <a href="https://www.semanticscholar.org">Semantic Scholar</a>, a website which aggregates and indexes research papers. Each citation was geocoded using names of institutions found in the PDF front matter, or as listed on other resources. These papers have been manually verified to show that researchers downloaded and used the dataset to train or test machine learning algorithms. If you use our data, please <a href="/about/attribution">cite our work</a>.
</p>
<div class="applet" data-payload="{"command": "citations"}"></div>
</section><section>
<div class="hr-wave-holder">
<div class="hr-wave-line hr-wave-line1"></div>
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<h2>Supplementary Information</h2>
</section><section>
<h4>Cite Our Work</h4>
<p>
If you find this analysis helpful, please cite our work:
<pre id="cite-bibtex">
@online{megapixels,
author = {Harvey, Adam. LaPlace, Jules.},
title = {MegaPixels: Origins, Ethics, and Privacy Implications of Publicly Available Face Recognition Image Datasets},
year = 2019,
url = {https://megapixels.cc/},
urldate = {2019-04-18}
}</pre>
</p>
</section>
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