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------------
status: published
title: Labeled Faces in The Wild
desc: Labeled Faces in The Wild (LFW) is a database of face photographs designed for studying the problem of unconstrained face recognition
subdesc: It includes 13,456 images of 4,432 people’s images copied from the Internet during 2002-2004.
slug: lfw
published: 2019-2-23
updated: 2019-2-23
color: #00FF00
authors: Adam Harvey
------------
# LFW
+ Years: 2002-2004
+ Images: 13,233
+ Identities: 5,749
+ Origin: Yahoo News Images
+ Funding: (Possibly, partially CIA*)
*Labeled Faces in The Wild* (LFW) is "a database of face photographs designed for studying the problem of unconstrained face recognition[^lfw_www]. It is used to evaluate and improve the performance of facial recognition algorithms in academic, commercial, and government research. According to BiometricUpdate.com[^lfw_pingan], 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](/datasets/ucd_faces/). Images obtained "in the wild" means using an image without explicit consent or awareness from the subject or photographer.
### Analysis
- There are about 3 men for every 1 woman (4,277 men and 1,472 women) in the LFW dataset[^lfw_www]
- The person with the most images is [George W. Bush](http://vis-www.cs.umass.edu/lfw/person/George_W_Bush_comp.html) with 530
- There are about 3 George W. Bush's for every 1 [Tony Blair](http://vis-www.cs.umass.edu/lfw/person/Tony_Blair.html)
- 70% of people in the dataset have only 1 image and 29% have 2 or more images
- The LFW dataset includes over 500 actors, 30 models, 10 presidents, 124 basketball players, 24 football players, 11 kings, 7 queens, and 1 [Moby](http://vis-www.cs.umass.edu/lfw/person/Moby.html)
- 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
### 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.
### 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 [SemanticScholar](https://www.semanticscholar.org).
[add map here]
### Citations
Browse or download the geocoded citation data collected for the LFW dataset.
[add citations table here]
### Additional Information
(tweet-sized snippets go here)
- The LFW dataset is considered the "most popular benchmark for face recognition" [^lfw_baidu]
- The LFW dataset is "the most widely used evaluation set in the field of facial recognition" [^lfw_pingan]
- All images in LFW dataset were obtained "in the wild" meaning without any consent from the subject or from the photographer
- The faces in the LFW dataset were detected using the Viola-Jones haarcascade face detector [^lfw_website] [^lfw-survey]
- 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." [^lfw_pingan]
- All images in the LFW dataset were copied from Yahoo News between 2002 - 2004
<<<<<<< HEAD
- In 2014, two of the four original authors of the LFW dataset received funding from IARPA and ODNI for their follow up paper [Labeled Faces in the Wild: Updates and New Reporting Procedures](https://www.semanticscholar.org/paper/Labeled-Faces-in-the-Wild-%3A-Updates-and-New-Huang-Learned-Miller/2d3482dcff69c7417c7b933f22de606a0e8e42d4) via IARPA contract number 2014-14071600010
- The dataset includes 2 images of [George Tenet](http://vis-www.cs.umass.edu/lfw/person/George_Tenet.html), 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
=======
- In 2014, 2/4 of the original authors of the LFW dataset received funding from IARPA and ODNI for their follow up paper "Labeled Faces in the Wild: Updates and New Reporting Procedures" via IARPA contract number 2014-14071600010
- The LFW dataset was used Center for Intelligent Information Retrieval, the Central Intelligence Agency, the National Security Agency and National
TODO (need citations for the following)
- SenseTime, who has relied on LFW for benchmarking their facial recognition performance, is one the leading provider of surveillance to the Chinese Government [need citation for this fact. is it the most? or is that Tencent?]
- Two out of 4 of the original authors received funding from the Office of Director of National Intelligence and IARPA for their 2016 LFW survey follow up report
>>>>>>> 13d7a450affe8ea4f368a97ea2014faa17702a4c
## Code
The LFW dataset is so widely used that a popular code library called Sci-Kit Learn includes a function called `fetch_lfw_people` to download the faces in the LFW dataset.
```python
#!/usr/bin/python
# ------------------------------------------------------------
#
# Script to generate montage of LFW faces used in scikit-learn
#
# ------------------------------------------------------------
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)
```
### Supplementary Material
```
load_file assets/lfw_commercial_use.csv
name_display, company_url, example_url, country, description
```
Text and graphics ©Adam Harvey / megapixels.cc
-------
Ignore text below these lines
-------
Research
- "In our experiments, we used 10000 images and associated captions from the Faces in the wilddata set [3]."
- "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."
- 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>
- 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
- 10f17534dba06af1ddab96c4188a9c98a020a459
- 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.
- From "Labeled Faces in the Wild: Updates and New Reporting Procedures"
[^lfw_www]: <http://vis-www.cs.umass.edu/lfw/results.html>
[^lfw_baidu]: Jingtuo Liu, Yafeng Deng, Tao Bai, Zhengping Wei, Chang Huang. Targeting Ultimate Accuracy: Face Recognition via Deep Embedding. <https://arxiv.org/abs/1506.07310>
[^lfw_pingan]: 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>
|
