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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

Sed ut perspiciatis, unde omnis iste natus error sit voluptatem accusantium doloremque laudantium, totam rem aperiam eaque ipsa, quae ab illo inventore veritatis et quasi architecto beatae vitae dicta sunt, explicabo. Nemo enim ipsam voluptatem, quia voluptas sit, aspernatur aut odit aut fugit, sed quia.

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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
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-import numpy as np
-from sklearn.datasets import fetch_lfw_people
-import imageio
-import imutils
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-# download LFW dataset (first run takes a while)
-lfw_people = fetch_lfw_people(min_faces_per_person=1, resize=1, color=True, funneled=False)
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-# 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
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-# 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]
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-# save full montage image
-imageio.imwrite('lfw_montage_full.png', montage)
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-# make a smaller version
-montage = imutils.resize(montage, width=960)
-imageio.imwrite('lfw_montage_960.jpg', montage)
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Research, text, and graphics ©Adam Harvey / megapixels.cc

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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