From b73e233acec5ad6c3aca7475288482f366f7a31f Mon Sep 17 00:00:00 2001 From: adamhrv Date: Fri, 5 Apr 2019 13:17:05 +0200 Subject: never say final, update uccs --- site/public/datasets/lfw/index.html | 122 +++++++++++++++++++----------------- 1 file changed, 65 insertions(+), 57 deletions(-) (limited to 'site/public/datasets/lfw') diff --git a/site/public/datasets/lfw/index.html b/site/public/datasets/lfw/index.html index d451d0cd..cb487913 100644 --- a/site/public/datasets/lfw/index.html +++ b/site/public/datasets/lfw/index.html @@ -42,45 +42,45 @@
Website
umass.edu
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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
umass.edu
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Labeled Faces in the Wild

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(PAGE UNDER DEVELOPMENT)

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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 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."

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Labeled Faces in the Wild

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[ PAGE UNDER DEVELOPMENT ]

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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 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.

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

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.

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.

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Who used LFW?

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+ 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. +

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Biometric Trade Routes

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- To help understand how LFW has been used around the world for commercial, military and academic research; publicly available research citing Labeled Faces in the Wild is collected, verified, and geocoded to show the biometric trade routes of people appearing in the images. Click on the markers to reveal reserach projects at that location. + To help understand how LFW has been used around the world by commercial, military, and academic organizations; existing publicly available research citing Labeled Faces in the Wild was collected, verified, and geocoded to show the biometric trade routes of people appearing in the images. Click on the markers to reveal research projects at that location.

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  • Academic
  • Commercial
  • Military / Government
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  • Citation data is collected using SemanticScholar.org then dataset usage verified and geolocated.
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    Citation data is collected using SemanticScholar.org then dataset usage verified and geolocated.
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    Who used LFW?

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    + +

    Dataset Citations

    - 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. + The dataset citations used in the visualizations were collected from Semantic Scholar, 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.

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    @@ -119,33 +108,52 @@
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    Supplementary Information

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    Supplementary Information

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    Dataset Citations

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    - The dataset citations used in the visualizations were collected from Semantic Scholar, 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. -

    - -

    Commercial Use

    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.

    -

    Research, text, and graphics ©Adam Harvey / megapixels.cc

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    Research

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    • "In our experiments, we used 10000 images and associated captions from the Faces in the wilddata set [3]."
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    • "This work was supported in part by the Center for Intelligent Information Retrieval, the Central Intelligence Agency, the National Security Agency and National Science Foundation under CAREER award IIS-0546666 and grant IIS-0326249."
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    • From: "People-LDA: Anchoring Topics to People using Face Recognition" https://www.semanticscholar.org/paper/People-LDA%3A-Anchoring-Topics-to-People-using-Face-Jain-Learned-Miller/10f17534dba06af1ddab96c4188a9c98a020a459 and https://ieeexplore.ieee.org/document/4409055
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    • This paper was presented at IEEE 11th ICCV conference Oct 14-21 and the main LFW paper "Labeled Faces in the Wild: A Database for Studying Face Recognition in Unconstrained Environments" was also published that same year
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    • 10f17534dba06af1ddab96c4188a9c98a020a459
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    • This research is based upon work supported in part by the Office of the Director of National Intelligence (ODNI), Intelligence Advanced Research Projects Activity (IARPA), via contract number 2014-14071600010.
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    • From "Labeled Faces in the Wild: Updates and New Reporting Procedures"
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    • 70% of people in the dataset have only 1 image and 29% have 2 or more images
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    • The LFW dataset is considered the "most popular benchmark for face recognition" 2
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    • The LFW dataset is "the most widely used evaluation set in the field of facial recognition" 3
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    • All images in LFW dataset were obtained "in the wild" meaning without any consent from the subject or from the photographer
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    • The faces in the LFW dataset were detected using the Viola-Jones haarcascade face detector [^lfw_website] [^lfw-survey]
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    • 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." 3
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    • All images in the LFW dataset were copied from Yahoo News between 2002 - 2004
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    • In 2014, two of the four original authors of the LFW dataset received funding from IARPA and ODNI for their followup paper Labeled Faces in the Wild: Updates and New Reporting Procedures via IARPA contract number 2014-14071600010
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    • The dataset includes 2 images of George Tenet, 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
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    • ./15/155205b8e288fd49bf203135871d66de879c8c04/paper.txt shows usage by DSTO Australia, supported parimal@iisc.ac.in
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    +
    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
      +
    • There are about 3 men for every 1 woman in the LFW dataset 1
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    • The person with the most images is George W. Bush with 530
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    • There are about 3 George W. Bush's for every 1 Tony Blair
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    • The LFW dataset includes over 500 actors, 30 models, 10 presidents, 124 basketball players, 24 football players, 11 kings, 7 queens, and 1 Moby
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    • In all 3 of the LFW publications [^lfw_original_paper], [^lfw_survey], [^lfw_tech_report] the words "ethics", "consent", and "privacy" appear 0 times
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    • The word "future" appears 71 times
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    • * denotes partial funding for related research
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    MegaPixels ©2017-19 Adam R. Harvey /  -- cgit v1.2.3-70-g09d2 From 9b1e2709cbdb40eabb34d379df18e61c10e3737c Mon Sep 17 00:00:00 2001 From: Jules Laplace Date: Thu, 11 Apr 2019 18:44:21 +0200 Subject: add h3 --- megapixels/app/site/parser.py | 7 +++-- .../datasets/50_people_one_question/index.html | 2 +- site/public/datasets/brainwash/index.html | 4 +-- site/public/datasets/duke_mtmc/index.html | 8 ++--- site/public/datasets/hrt_transgender/index.html | 2 +- site/public/datasets/index.html | 36 ---------------------- site/public/datasets/lfw/index.html | 4 +-- site/public/datasets/msceleb/index.html | 4 +-- site/public/datasets/oxford_town_centre/index.html | 11 ++++--- site/public/datasets/uccs/index.html | 6 ++-- 10 files changed, 27 insertions(+), 57 deletions(-) (limited to 'site/public/datasets/lfw') diff --git a/megapixels/app/site/parser.py b/megapixels/app/site/parser.py index dc2a09f2..6b71e041 100644 --- a/megapixels/app/site/parser.py +++ b/megapixels/app/site/parser.py @@ -145,8 +145,11 @@ def parse_markdown(metadata, sections, s3_path, skip_h1=False): return ' {}'.format(key, footnote_count, key, index, index) key_regex = re.compile(key.replace('[', '\\[').replace('^', '\\^').replace(']', '\\]')) content = key_regex.sub(footnote_tag, content) - footnote_txt = footnote_txt.replace("{}_BACKLINKS".format(index), "".join(footnote_backlinks)) + footnote_txt = footnote_txt.replace("{}_BACKLINKS".format(key), "".join(footnote_backlinks)) + content += '
    ' + content += '

    References

    ' content += footnote_txt + content += '
    ' return content @@ -254,7 +257,7 @@ def format_footnotes(footnotes, s3_path): continue key, note = footnote.split(': ', 1) footnote_index_lookup[key] = index - footnote_list.append('{}_BACKLINKS'.format(key, index) + markdown(note)) + footnote_list.append('{}_BACKLINKS'.format(key, key) + markdown(note)) index += 1 footnote_txt = '
    • ' + '
    • '.join(footnote_list) + '
    ' diff --git a/site/public/datasets/50_people_one_question/index.html b/site/public/datasets/50_people_one_question/index.html index b27fa3e5..3b33f530 100644 --- a/site/public/datasets/50_people_one_question/index.html +++ b/site/public/datasets/50_people_one_question/index.html @@ -35,7 +35,7 @@
    33
    Purpose
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    Facial landmark estimation in the wild
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    Facial landmark estimation
    Website
    diff --git a/site/public/datasets/brainwash/index.html b/site/public/datasets/brainwash/index.html index 10ee577c..bd59f573 100644 --- a/site/public/datasets/brainwash/index.html +++ b/site/public/datasets/brainwash/index.html @@ -120,11 +120,11 @@
  • add ethics link to Stanford
  • add optout info
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    References

    • a

      "readme.txt" https://exhibits.stanford.edu/data/catalog/sx925dc9385.

    • a

      Stewart, Russel. Andriluka, Mykhaylo. "End-to-end people detection in crowded scenes". 2016.

    • a

      Li, Y. and Dou, Y. and Liu, X. and Li, T. Localized Region Context and Object Feature Fusion for People Head Detection. ICIP16 Proceedings. 2016. Pages 594-598.

    • a

      Zhao. X, Wang Y, Dou, Y. A Replacement Algorithm of Non-Maximum Suppression Base on Graph Clustering.

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    +
    diff --git a/site/public/datasets/duke_mtmc/index.html b/site/public/datasets/duke_mtmc/index.html index 0d082c15..9bec47ed 100644 --- a/site/public/datasets/duke_mtmc/index.html +++ b/site/public/datasets/duke_mtmc/index.html @@ -35,10 +35,10 @@
    2,000,000
    Identities
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    1,812
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    2,700
    Purpose
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    Person re-identification and multi-camera tracking
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    Person re-identification, multi-camera tracking
    Created by
    Computer Science Department, Duke University, Durham, US
    @@ -112,7 +112,7 @@

    Notes

    The Duke MTMC dataset paper mentions 2,700 identities, but their ground truth file only lists annotations for 1,812

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    References

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