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- Datasets - Research - About -

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Style Examples

Alt text here

Date

17-Jan-2019

Numbers

Identities

12,139

But also

This is a test of the stylesheet

Header 1

Header 2

Header 3

Header 4

Header 5

Header 6

Bold text, italic text, bold italic text

At vero eos et et iusto qui blanditiis praesentium voluptatum deleniti atque corrupti[^1], quos dolores et quas molestias excepturi sint, obcaecati cupiditate non-provident, similique sunt in culpa, qui officia deserunt mollitia animi, id est laborum et dolorum fuga. Et harum quidem rerum facilis est et expedita distinctio[^2]. Nam libero tempore, cum soluta nobis est eligendi optio, cumque nihil impedit, quo minus id, quod maxime placeat, facere possimus, omnis voluptas assumenda est, omnis dolor repellendus[^3].

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 consequuntur magni dolores eos
Qui ratione voluptatem sequi nesciunt, neque porro quisquam

single image test

This person is alone

double image test

This person is on the left

This person is on the right

triple image test

Person 1

Person 2

Person 3. Let me tell you about Person 3. This person has a very long description with text which wraps like crazy

est, qui dolorem ipsum, quia dolor sit amet consectetur adipisci[ng] velit, sed quia non-numquam [do] eius modi tempora inci[di]dunt, ut labore et dolore magnam aliquam quaerat voluptatem.
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This image is extremely wide and the text beneath it will wrap but thats fine because it can also contain <a href="https://example.com/">hyperlinks</a>! Yes, you read that right—hyperlinks! Lorem ipsum dolor sit amet ad volotesque sic hoc ad nauseam

This image is extremely wide and the text beneath it will wrap but that's fine because it can also contain hyperlinks! Yes, you read that right—hyperlinks! Lorem ipsum dolor sit amet ad volotesque sic hoc ad nauseam

Inline code has back-ticks around it.

s = "Python syntax highlighting"
-print(s)
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Horizontal rule

Citations below here

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MegaPixels

- The Darkside of Datasets - -

- Face Search - Datasets - Research - About -

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Labeled Faces in The Wild

Created 2007 (auto)
Images 13,233 (auto)
People 5,749 (auto)
Created From Yahoo News images (auto)
Analyzed and searchable (auto)

Labeled Faces in The Wild is amongst the most widely used facial recognition training datasets in the world and is the first facial recognition dataset [^lfw_names_faces] of its kind to be created entirely from Internet photos. It includes 13,233 images of 5,749 people that appeared on Yahoo News between 2002 - 2004.

Eight out of 5,749 people in the Labeled Faces in the Wild dataset. The face recognition training dataset is created entirely from photos downloaded from the Internet.

INTRO

It began in 2002. Researchers at University of Massachusetts Amherst were developing algorithms for facial recognition and they needed more data. Between 2002-2004 they scraped Yahoo News for images of public figures. Two years later they cleaned up the dataset and repackaged it as Labeled Faces in the Wild (LFW).

Since then the LFW dataset has become one of the most widely used datasets used for evaluating face recognition algorithms. The associated research paper “Labeled Faces in the Wild: A Database for Studying Face Recognition in Unconstrained Environments” has been cited 996 times reaching 45 different countries throughout the world.

The faces come from news stories and are mostly celebrities from the entertainment industry, politicians, and villains. It’s a sampling of current affairs and breaking news that has come to pass. The images, detached from their original context now server a new purpose: to train, evaluate, and improve facial recognition.

As the most widely used facial recognition dataset, it can be said that each individual in LFW has, in a small way, contributed to the current state of the art in facial recognition surveillance. John Cusack, Julianne Moore, Barry Bonds, Osama bin Laden, and even Moby are amongst these biometric pillars, exemplar faces provided the visual dimensions of a new computer vision future.

From Aaron Eckhart to Zydrunas Ilgauskas. A small sampling of the LFW dataset

In addition to commercial use as an evaluation tool, all of the faces in LFW dataset are prepackaged into a popular machine learning code framework called scikit-learn.

Usage

#!/usr/bin/python
-from matplotlib import plt
-from sklearn.datasets import fetch_lfw_people
-lfw_people = fetch_lfw_people()
-lfw_person = lfw_people[0]
-plt.imshow(lfw_person)
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Commercial Use

The LFW dataset is used by numerous companies for benchmarking algorithms and in some cases training. According to the benchmarking results page [^lfw_results] provided by the authors, over 2 dozen companies have contributed their benchmark results

load file: lfw_commercial_use.csv
-name_display,company_url,example_url,country,description
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Company	Country	Industries
Aratek	China	Biometric sensors for telecom, civil identification, finance, education, POS, and transportation
Aratek	China	Biometric sensors for telecom, civil identification, finance, education, POS, and transportation
Aratek	China	Biometric sensors for telecom, civil identification, finance, education, POS, and transportation

Add 2-4 screenshots of companies mentioning LFW here

ReadSense

In benchmarking, companies use a dataset to evaluate their algorithms which are typically trained on other data. After training, researchers will use LFW as a benchmark to compare results with other algorithms.

For example, Baidu (est. net worth $13B) uses LFW to report results for their "Targeting Ultimate Accuracy: Face Recognition via Deep Embedding". According to the three Baidu researchers who produced the paper:

LFW has been the most popular evaluation benchmark for face recognition, and played a very important role in facilitating the face recognition society to improve algorithm. ¹.
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Citations

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Title	Organization	Country	Type
3D-aided face recognition from videos	University of Lyon	France	edu
A Community Detection Approach to Cleaning Extremely Large Face Database	National University of Defense Technology, China	China	edu

Conclusion

The LFW face recognition training and evaluation dataset is a historically important face dataset as it was the first popular dataset to be created entirely from Internet images, paving the way for a global trend towards downloading anyone’s face from the Internet and adding it to a dataset. As will be evident with other datasets, LFW’s approach has now become the norm.

For all the 5,000 people in this datasets, their face is forever a part of facial recognition history. It would be impossible to remove anyone from the dataset because it is so ubiquitous. For their rest of the lives and forever after, these 5,000 people will continue to be used for training facial recognition surveillance.

Notes

According to BiometricUpdate.com², 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."

"Chinese tourist town uses face recognition as an entry pass". New Scientist. November 17, 2016. https://www.newscientist.com/article/2113176-chinese-tourist-town-uses-face-recognition-as-an-entry-pass/↩
"PING AN Tech facial recognition receives high score in latest LFW test results". https://www.biometricupdate.com/201702/ping-an-tech-facial-recognition-receives-high-score-in-latest-lfw-test-results ↩

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MegaPixels

- The Darkside of Datasets - -

- Face Search - Datasets - Research - About -

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From 1 to 100 Pixels

Posted

2018-12-04

Adam Harvey
Berit Gilma
Matthew Stender

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High resolution insights from low resolution data

This post will be about the meaning of "face". How do people define it? How to biometrics researchers define it? How has it changed during the last decade.

What can you know from a very small amount of information?

1 pixel grayscale
2x2 pixels grayscale, font example
4x4 pixels
8x8 yotta yotta
5x7 face recognition
12x16 activity recognition
6/5 (up to 124/106) pixels in height/width, and the average is 24/20 for QMUL SurvFace
20x16 tiny faces paper
20x20 MNIST handwritten images http://yann.lecun.com/exdb/mnist/
24x24 haarcascade detector idealized images
32x32 CIFAR image dataset
40x40 can do emotion detection, face recognition at scale, 3d modeling of the face. include datasets with faces at this resolution including pedestrian.
need more material from 60-100
60x60 show how texture emerges and pupils, eye color, higher resolution of features and compare to lower resolution faces
100x100 0.5% of one Instagram photo

Find specific cases of facial resolution being used in legal cases, forensic investigations, or military footage

Research

NIST report on sres states several resolutions
"Results show that the tested face recognition systems yielded similar performance for query sets with eye-to-eye distance from 60 pixels to 30 pixels" ¹

NIST 906932. Performance Assessment of Face Recognition Using Super-Resolution. Shuowen Hu, Robert Maschal, S. Susan Young, Tsai Hong Hong, Jonathon P. Phillips↩

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MORE RESEARCH

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