Date: Mon, 4 Mar 2019 22:20:36 +0100 Subject: cosmetics --- site/assets/css/css.css | 129 +++++++++++++++++++-------- site/assets/css/splash.css | 9 +- site/assets/img/megapixels_logo.svg | 14 --- site/assets/img/megapixels_logo_white.svg | 10 +-- site/includes/map.html | 5 +- site/includes/supplementary_header.html | 10 +++ site/public/about/credits/index.html | 59 ------------- site/public/about/disclaimer/index.html | 65 -------------- site/public/about/index.html | 58 ------------ site/public/about/press/index.html | 62 ------------- site/public/about/privacy/index.html | 141 ------------------------------ site/public/about/research/index.html | 59 ------------- site/public/about/terms/index.html | 80 ----------------- site/public/datasets/index.html | 78 ----------------- site/public/datasets/lfw/index.html | 111 ----------------------- site/public/datasets/vgg_face2/index.html | 74 ---------------- site/public/index.html | 39 --------- site/templates/home.html | 7 +- 18 files changed, 118 insertions(+), 892 deletions(-) delete mode 100644 site/assets/img/megapixels_logo.svg mode change 100644 => 100755 site/assets/img/megapixels_logo_white.svg create mode 100644 site/includes/supplementary_header.html delete mode 100644 site/public/about/credits/index.html delete mode 100644 site/public/about/disclaimer/index.html delete mode 100644 site/public/about/index.html delete mode 100644 site/public/about/press/index.html delete mode 100644 site/public/about/privacy/index.html delete mode 100644 site/public/about/research/index.html delete mode 100644 site/public/about/terms/index.html delete mode 100644 site/public/datasets/index.html delete mode 100644 site/public/datasets/lfw/index.html delete mode 100644 site/public/datasets/vgg_face2/index.html delete mode 100644 site/public/index.html diff --git a/site/assets/css/css.css b/site/assets/css/css.css index 723b0f08..b9ae0a4f 100644 --- a/site/assets/css/css.css +++ b/site/assets/css/css.css @@ -11,10 +11,11 @@ html, body { html { background: #181818; } - +a {outline : none;} +img {border : 0;} .content { opacity: 0; - transition: opacity 0.2s cubic-bezier(0,1,1,1); + transition: opacity 0.1s cubic-bezier(0,1,1,1); } html.desktop .content, html.mobile .content { @@ -48,16 +49,28 @@ header .logo { background-image: url(../img/megapixels_logo_white.svg); background-size: contain; background-repeat: no-repeat; - margin-top: 7px; - margin-right: 10px; - width: 39px; - height: 30px; + margin-top: 0px; + margin-right: 8px; + width: 20px; + height: 20px; } header .site_name { font-family: 'Roboto', sans-serif; - font-weight: bold; + font-weight: 600; + text-transform: uppercase;; color: #fff; - font-size: 14px; + font-size: 12px; + line-height: 12px; + letter-spacing: 4px; +} +header .site_name.splash{ + font-size: 20px; + line-height: 20px; + font-weight: 400; +} +header .links{ + font-size: 18px; + line-height: 18px; } header .sub { margin-left: 4px; @@ -82,25 +95,30 @@ header .links { header .links span, header .links a { display: block; - color: #777; + color: #d7ba5a; text-decoration: none; text-transform: uppercase; margin-right: 32px; - transition: color 0.1s cubic-bezier(0,0,1,1), border-color 0.1s cubic-bezier(0,0,1,1); + transition: color 0.1s cubic-bezier(0,0,1,1), border-color 0.05s cubic-bezier(0,0,1,1); border-bottom: 1px solid rgba(255,255,255,0); padding: 3px; - font-weight: 500; + font-weight: 400; } header .links a.active { - color: #bbb; + color: #f3c266; + border-bottom: 1px solid rgba(240,200,150,255); } .desktop header .links a:hover { - color: #fff; - border-bottom: 1px solid rgba(255,255,255,255); + color: #e9bb5f; + border-bottom: 1px solid rgba(240,200,150,255); } .desktop header .links a.active:hover { - color: #fff; - border-bottom: 1px solid rgba(255,255,255,255); + color: #e9bb5f; + border-bottom: 1px solid rgba(240,200,150,255); +} +header .links.splash{ + font-size:22px; + font-weight: 400; } /* footer */ @@ -129,7 +147,7 @@ footer > div:nth-child(2) { footer a { display: inline-block; color: #888; - transition: color 0.2s cubic-bezier(0,0,1,1); + transition: color 0.1s cubic-bezier(0,0,1,1); margin-right: 5px; } footer a:hover { @@ -144,7 +162,7 @@ h1 { font-size: 28pt; margin: 75px 0 10px; padding: 0; - transition: color 0.2s cubic-bezier(0,0,1,1); + transition: color 0.1s cubic-bezier(0,0,1,1); font-family: 'Roboto'; } h2 { @@ -153,15 +171,15 @@ h2 { font-size: 28pt; margin: 20px 0 10px; padding: 0; - transition: color 0.2s cubic-bezier(0,0,1,1); + transition: color 0.1s cubic-bezier(0,0,1,1); font-family: 'Roboto'; } h3 { margin: 0 0 20px 0; padding: 20px 0 0 0; - font-size: 16pt; + font-size: 18pt; font-weight: 500; - transition: color 0.2s cubic-bezier(0,0,1,1); + transition: color 0.1s cubic-bezier(0,0,1,1); font-family: 'Roboto'; } h4 { @@ -169,7 +187,7 @@ h4 { padding: 0; font-size: 11pt; font-weight: 500; - transition: color 0.2s cubic-bezier(0,0,1,1); + transition: color 0.1s cubic-bezier(0,0,1,1); font-family: 'Roboto'; } .content h3 a { @@ -206,7 +224,7 @@ th, .gray { /* content */ .content { - padding-top: 45px; + padding-top: 28px; padding-bottom: 100px; min-height: calc(100vh - 55px); line-height: 1.5; @@ -227,7 +245,7 @@ section { line-height: 36px; } p { - margin: 0 0 20px 0; + margin: 0 10px 20px 0; line-height: 2; font-size: 16px; font-weight: 300; @@ -236,7 +254,7 @@ p { color: #fff; text-decoration: none; border-bottom: 1px dashed; - transition: color 0.2s cubic-bezier(0,0,1,1); + transition: color 0.1s cubic-bezier(0,0,1,1); } .desktop .content a:hover { color: #fff; @@ -278,7 +296,7 @@ p { padding-top: 10px; padding-right: 20px; margin-right: 20px; - margin-bottom: 10px; + margin-bottom: 30px; /*border-right: 1px solid #444;*/ font-family: 'Roboto'; font-size: 14px; @@ -386,7 +404,7 @@ section img { margin: 0 auto; } section .image { - margin-bottom: 40px; + margin-bottom: 20px; } section.images { display: flex; @@ -440,12 +458,20 @@ section.fullwidth .image { } .caption { text-align: left; - font-size: 9pt; + font-size: 10pt; color: #999; max-width: 960px; margin: 10px auto 10px auto; font-family: 'Roboto'; } +.caption a{ + color:#ccc; + border: 0; +} +.caption a:hover{ + color:#fff; + border: 0; +} .sideimage { margin: 0px 0 40px 0; display: flex; @@ -595,8 +621,8 @@ section.fullwidth .image { height: 140px; padding: 10px; color: white; - font-weight: 300; - font-family: 'Roboto Mono' + font-weight: 400; + font-family: 'Roboto' } .desktop .content .dataset-list a{ border:none; @@ -649,13 +675,19 @@ section.intro_section { font-size: 17px; line-height: 36px; max-width: 680px; - font-weight: 400; + font-weight: 300; color: #ddd; } -.intro_section div > span { - box-shadow: -10px -10px #181818, 10px -10px #181818, 10px 10px #181818, -10px 10px #181818; +.intro_section .hero_desc .bgpad { + box-shadow: -7px -7px #181818, 7px -7px #181818, 7px 7px #181818, -7px 7px #181818; background: #181818; } +.intro_section .hero_subdesc .bgpad { + box-shadow: -8px -8px #181818, 8px -8px #181818, 8px 8px #181818, -8px 8px #181818; + background: #181818; +} + + .firefox .intro_section div > span { box-decoration-break: clone; } @@ -670,6 +702,29 @@ page-specific formatting margin-bottom: 10px } +.hr-wave-holder { + /* Clip edges, as some of the lines don't terminate nicely. */ + overflow: hidden; + position: relative; + width: 100%; + height: 50px; + margin-top:100px; +} + +.hr-wave-line1 { + background: linear-gradient(45deg, transparent, transparent 49%, #fff 49%, transparent 51%); +} +.hr-wave-line2 { + background: linear-gradient(-45deg, transparent, transparent 49%, #fff 49%, transparent 51%); +} +.hr-wave-line { + background-size: 20px 20px; + position: absolute; + width: 100%; + height: 10px; +} + + /* map */ .map-legend-item{ @@ -693,14 +748,16 @@ page-specific formatting } .content-about .about-menu ul li{ display:inline-block; - font-size: 12px; + font-size: 14px; font-weight: 400; - margin-right: 5px; + margin-right: 8px; text-transform: uppercase; - margin-right: 15px + margin-right: 15px; + font-family: 'Roboto'; } .content-about .about-menu ul li a{ border-bottom: 0; + color:#aaa; } diff --git a/site/assets/css/splash.css b/site/assets/css/splash.css index 9dd5afba..8b07918b 100644 --- a/site/assets/css/splash.css +++ b/site/assets/css/splash.css @@ -18,14 +18,15 @@ footer { footer div { z-index: 2; } -header, footer { - text-shadow: 0 0 5px #191919, 0 0 5px #191919, 0 0 5px #191919; +footer { + /*text-shadow: 0 0 5px #191919, 0 0 5px #191919, 0 0 5px #191919;*/ + background: #111; } header > div a, footer > div { - background: rgba(25, 25, 25, 0.8); + /*background: rgba(25, 25, 25, 0.8); padding: 3px; - border-radius: 2px; + border-radius: 2px;*/ } header .links a.activeLink { color: white; diff --git a/site/assets/img/megapixels_logo.svg b/site/assets/img/megapixels_logo.svg deleted file mode 100644 index a9b7eff2..00000000 --- a/site/assets/img/megapixels_logo.svg +++ /dev/null @@ -1,14 +0,0 @@ - - - - diff --git a/site/assets/img/megapixels_logo_white.svg b/site/assets/img/megapixels_logo_white.svg old mode 100644 new mode 100755 index 061e30ab..00aa430f --- a/site/assets/img/megapixels_logo_white.svg +++ b/site/assets/img/megapixels_logo_white.svg @@ -2,13 +2,9 @@ diff --git a/site/includes/map.html b/site/includes/map.html index 730e30d0..44550f90 100644 --- a/site/includes/map.html +++ b/site/includes/map.html @@ -12,7 +12,7 @@ -->

- 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 Semantic Scholar. + 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..

@@ -24,5 +24,6 @@

■ Academic

■ Industry

■ Government

+ Data is compiled from Semantic Scholar

diff --git a/site/includes/supplementary_header.html b/site/includes/supplementary_header.html new file mode 100644 index 00000000..5fd4b2b4 --- /dev/null +++ b/site/includes/supplementary_header.html @@ -0,0 +1,10 @@ +

+ + +

+ +

Supplementary Information

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MegaPixels is an art and research project by Adam Harvey about the origins and ethics of facial analysis datasets. Where do they come from? Who's included? Who created it and for what reason?

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Jules LaPlace is an American artist and technologist also based in Berlin. He was previously the CTO of a NYC digital agency and currently works at VFRAME, developing computer vision for human rights groups, and building creative software for artists.

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Aug 22, 2018: "Transgender YouTubers had their videos grabbed to train facial recognition software" by James Vincent https://www.theverge.com/2017/8/22/16180080/transgender-youtubers-ai-facial-recognition-dataset

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Facial Recognition Datasets

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- Brainwash -

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- Caltech Occluded Faces in The Wild -

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- Labeled Faces in The Wild -

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Labeled Faces in The Wild (LFW) is a database of face photographs designed for studying the problem of unconstrained face recognition.

It includes 13,456 images of 4,432 people's images copied from the Internet during 2002-2004. -

A few of the 5,749 people in the Labeled Faces in the Wild Dataset, thee most widely used face dataset for benchmarking face recognition algorithms.

Created

2002-2004

Images

13,233

Identities

5,749

Origin

Yahoo! News Images

Used by

Facebook, Google, Microsoft, Baidu, Tencent, SenseTime, Face++, CIA, NSA, IARPA

Website

vis-www.cs.umass.edu/lfw

There are about 3 men for every 1 woman in the LFW dataset 1
The person with the most images is George W. Bush with 530
There are about 3 George W. Bush's for every 1 Tony Blair
The LFW dataset includes over 500 actors, 30 models, 10 presidents, 124 basketball players, 24 football players, 11 kings, 7 queens, and 1 Moby
In all 3 of the LFW publications [^lfw_original_paper], [^lfw_survey], [^lfw_tech_report] the words "ethics", "consent", and "privacy" appear 0 times
The word "future" appears 71 times
* denotes partial funding for related research

Labeled Faces in the Wild

Labeled Faces in The Wild (LFW) is "a database of face photographs designed for studying the problem of unconstrained face recognition 1. It is used to evaluate and improve the performance of facial recognition algorithms in academic, commercial, and government research. According to BiometricUpdate.com 3, 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."

The LFW dataset includes 13,233 images of 5,749 people that were collected between 2002-2004. LFW is a subset of Names of Faces and is part of the first facial recognition training dataset created entirely from images appearing on the Internet. The people appearing in LFW are...

The Names and Faces dataset was the first face recognition dataset created entire from online photos. However, Names and Faces and LFW are not the first face recognition dataset created entirely "in the wild". That title belongs to the UCD dataset. Images obtained "in the wild" means using an image without explicit consent or awareness from the subject or photographer.

All 5,379 people in the Labeled Faces in The Wild Dataset. Showing one face per person

Biometric Trade Routes

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 Semantic Scholar.

■ Academic

■ Industry

■ Government

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Supplementary Information for Labeled Faces in The Wild

Citations

Add graph showing distribution by country. Add information about how the citations were generated. Add button/link to download CSV

Synthetic Faces

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.

Synthetically generated face from the visual space of LFW dataset

Commercial Use of Labeled Faces in The Wild

Add a paragraph about how usage extends far beyond academia into research centers for largest companies in the world. And even funnels into CIA funded research in the US and defense industry usage in China.

Code

The LFW dataset is so widely used that access to the facial data has built directly into a popular code library called Sci-Kit Learn. It includes a function called fetch_lfw_people to download the faces in the LFW dataset.

#!/usr/bin/python
-
-import numpy as np
-from sklearn.datasets import fetch_lfw_people
-import imageio
-import imutils
-
-# download LFW dataset (first run takes a while)
-lfw_people = fetch_lfw_people(min_faces_per_person=1, resize=1, color=True, funneled=False)
-
-# introspect dataset
-n_samples, h, w, c = lfw_people.images.shape
-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))
-montage = montages[0]
-
-# save full montage image
-imageio.imwrite('lfw_montage_full.png', montage)
-
-# make a smaller version
-montage = imutils.resize(montage, width=960)
-imageio.imwrite('lfw_montage_960.jpg', montage)
-

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http://vis-www.cs.umass.edu/lfw/results.html
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Jingtuo Liu, Yafeng Deng, Tao Bai, Zhengping Wei, Chang Huang. Targeting Ultimate Accuracy: Face Recognition via Deep Embedding. https://arxiv.org/abs/1506.07310
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a
Lee, Justin. "PING AN Tech facial recognition receives high score in latest LFW test results". BiometricUpdate.com. Feb 13, 2017. https://www.biometricupdate.com/201702/ping-an-tech-facial-recognition-receives-high-score-in-latest-lfw-test-results
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VGG Face 2

Years

TBD

Images

TBD

Identities

TBD

Origin

TBD

Funding

IARPA

...

Analysis

The VGG Face 2 dataset includes approximately 1,331 actresses, 139 presidents, 16 wives, 3 husbands, 2 snooker player, and 1 guru

Names and descriptions

The original VGGF2 name list has been updated with the results returned from Google Knowledge
Names with a similarity score greater than 0.75 where automatically updated. Scores computed using import difflib; seq = difflib.SequenceMatcher(a=a.lower(), b=b.lower()); score = seq.ratio()
The 97 names with a score of 0.75 or lower were manually reviewed and includes name changes validating using Wikipedia.org results for names such as "Bruce Jenner" to "Caitlyn Jenner", spousal last-name changes, and discretionary changes to improve search results such as combining nicknames with full name when appropriate, for example changing "Aleksandar Petrović" to "Aleksandar 'Aco' Petrović" and minor changes such as "Mohammad Ali" to "Muhammad Ali"
The 'Description' text was automatically added when the Knowledge Graph score was greater than 250

TODO

create name list, and populate with Knowledge graph information like LFW
make list of interesting number stats, by the numbers
make list of interesting important facts
write intro abstract
write analysis of usage
find examples, citations, and screenshots of useage
find list of companies using it for table
create montages of the dataset, like LFW
create right to removal information

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