{% extends 'layout.html' %}

{% block content %}
  <div class='hero'>
    <div class='inner'>
      <div id="face_container">
        <div class='currentFace'></div>
      </div>
      <div class='intro'>
        <div class='headline'>
          MegaPixels is an art project that explores the dark side of face recognition datasets and the future of computer vision.
        </div>

        <div class='buttons'>
          <a href="/datasets/lfw/"><button class='important'>Find Your Face</button></a>
          <a href="/analyze/"><button class='normal'>Analyze Your Face</button></a>
        </div>

        <div class='under'>
          Made by Adam Harvey in collaboration with Jules Laplace, and in partnership with Mozilla.<br/>
          <a href='/about/'>Read more about MegaPixels</a>
        </div>
      </div>
    </div>
  </div>

  <section class='wide dataset-intro'>
    <h2>Face Recognition Datasets</h2>
    <div class='right-sidebar'>
      <h4>SUMMARY</h4>
      <div class='meta'>
        <div><div class='gray'>Found</div><div>275 datasets</div></div>
        <div><div class='gray'>Created between</div><div>1993-2018</div></div>
        <div><div class='gray'>Smallest dataset</div><div>20 images</div></div>
        <div><div class='gray'>Largest dataset</div><div>10,000,000 images</div></div>
        <div><div class='gray'>Highest resolution faces</div><div>450x500 (Unconstrained College Students)</div></div>
        <div><div class='gray'>Lowest resolution faces</div><div>16x20 pixels (QMUL SurvFace)</div></div>
      </div>
    </div>

    <p>
      MegaPixels is an online art project that explores the history of face recognition from the perspective of datasets. MegaPixels aims to unravel the meanings behind the data and expose the darker corners of the biometric industry that have contributed to its growth.
    </p>
    <p>
      Through a mix of case studies, visualizations, and interactive tools, Megapixels will use face recognition datasets to tell the history of modern biometrics.  Many people have contributed to the development of face recignition technology, both wittingly and unwittingly.  Not only scientists, but also celebrities and regular internet users have played a part.
    </p>
    <p>
      Face recognition is a mess of contradictinos.  It works, yet it doesn't actually work. It's cheap and accessible, but also expensive and out of control. Face recognition research has achieved headline grabbing superhuman accuracies over 99.9%, yet in practice it's also dangerously inaccurate.
    </p>
    <p>
      During a trial installation at Sudkreuz station in Berlin in 2018, 20% of the matches were wrong, a number so low that it should not have any connection to law enforcement or justice. And in London, the Metropolitan police had been using face recognition software that mistakenly identified an alarming 98% of people as criminals, which perhaps is a crime itself.
    </p>
  </section>

  <section class='wide dataset-intro'>
    <h2>Dataset Portraits</h2>
    <p>
      We have prepared detailed case studies of some of the more noteworthy datasets, including tools to help you learn what is contained in these datasets, and even whether your own face has been used to train these algorithms.
    </p>

    <div class="dataset-list">
    {% for dataset in datasets %}
      <a href="{{ dataset.url }}">
        <div class="dataset">
          {{ dataset.title }}
        </div>
      </a>
    {% endfor %}
    </div>
  </section>

{% endblock %}

{% block scripts %}
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<script src="/assets/js/app/face.js"></script>
{% endblock %}