Biometrics is a phrase derived from the Greek roots bio (life) and metrikos (measurement).
It is used to examine data in the biological sciences using
statistical or mathematical techniques.
In recent years, the phrase has been used in a more precise,
high-tech sense to refer to the science of identifying people based on
biological or behavioral features, as well as the artificial intelligence
technologies that are employed to do so.
For ages, scientists have been measuring human physical
characteristics or behaviors in order to identify them afterwards.
The first documented application of biometrics may be found
in the works of Portuguese historian Joao de Barros (1496–1570).
De Barros reported how Chinese merchants stamped and
recorded children's hands and footprints with ink.
Biometric methods were first used in criminal justice
settings in the late nineteenth century.
Alphonse Bertillon (1853–1914), a police clerk in Paris,
started gathering bodily measurements (head circumference, finger length, etc.)
of prisoners in jail to keep track of repeat criminals, particularly those who
used aliases or altered features of their appearance to prevent detection.
Bertillonage was the name given to his system.
After the 1890s, when it became clear that many people had
identical dimensions, it went out of favor.
Richard Edward Henry (1850–1931), of Scotland Yard, created
a significantly more successful biometric technique based on fingerprinting in
1901.
On the tips of people's fingers and thumbs, he measured and
categorized loops, whorls, and arches, as well as subcategories of these
components.
Fingerprinting is still one of the most often utilized
biometric identifiers by law enforcement authorities across the globe.
Fingerprinting systems are expanding in tandem with
networking technology, using vast national and international databases as well
as computer matching.
In the 1960s and 1970s, the Federal Bureau of Investigation
collaborated with the National Bureau of Standards to automate fingerprint
identification.
This included scanning existing paper fingerprint cards and
creating minutiae feature extraction algorithms and automatic classifiers for
comparing electronic fingerprint data.
Because of the high expense of electronic storage, the
scanned pictures of fingerprints, as well as the categorization data and
minutiae, were not kept in digital form.
In 1980, the FBI made the M40 fingerprint matching
technology operational.
In 1999, the Integrated Automated Fingerprint Identification
System (IAFIS) became live.
In 2014, the FBI's Next Generation Identification system, an
outgrowth of IAFIS, was used to record palm print, iris, and face
identification.
While biometric technology is often seen as a way to boost
security at the price of privacy, it may also be utilized to assist retain
privacy in specific cases.
Many sorts of health-care employees in hospitals need access
to a shared database of patient information.
The Health Insurance Portability and Accountability Act
emphasizes the need of preventing unauthorized individuals from accessing this
sensitive data (HIPAA).
For example, the Mayo Clinic in Florida was a pioneer in
biometric access to medical records.
In 1997, the clinic started utilizing digital fingerprinting
to limit access to patient information.
Today, voice analysis, face or iris recognition, hand
geometry, keystroke dynamics, gait, DNA, and even body odor combine with big
data and artificial intelligence recognition software to rap idly identify or
authenticate individuals based on voice analysis, face or iris recognition,
hand geometry, keystroke dynamics, gait, DNA, and even body odor.
The reliability of DNA fingerprinting has evolved to the
point that it is widely recognized by courts.
Even in the absence of further evidence, criminals have been
convicted based on DNA findings, while falsely incarcerated prisoners have been
exonerated.
While biometrics is frequently employed by law enforcement
agencies, courts, and other government agencies, it has also come under fire
from the public for infringing on individual privacy rights.
Biometric artificial intelligence software research has
risen in tandem with actual and perceived criminal and terrorist concerns at
universities, government agencies, and commercial enterprises.
National Bank United used technology developed by biometric
experts Visionics and Keyware Technologies to install iris recognition
identification systems on three ATMs in Texas as an experiment in 1999.
At Super Bowl XXXV in Tampa, Florida, Visage Corporation
presented the FaceFINDER System, an automatic face recognition device.
As fans entered the stadium, the technology scanned their
faces and matched them to a database of 1,700 known criminals and terrorists.
Officials claimed to have identified a limited number of
offenders, but there have been no big arrests or convictions as a result of
such identifications.
At the time, the indiscriminate use of automatic face
recognition sparked a lot of debate.
The Snooper Bowl was even dubbed after the game.
Following the terrorist events of September 11, 2001, a
public policy discussion in the United States focused on the adoption of
biometric technology for airport security.
Following 9/11, polls revealed that Americans were prepared
to give up significant portions of their privacy in exchange for increased
security.
Biometric technology were already widely used in other
nations, such as the Netherlands.
The Privium program for passenger iris scan verification has
been in effect at Schiphol Airport since 2001.
In 2015, the Transportation Security Administration (TSA) of
the United States started testing biometric techniques for identification
verification.
In 2019, Delta Air Lines, in collaboration with US Customs
and Border Protection, provided customers at Atlanta's Maynard Jackson
International Terminal the option of face recognition boarding.
Passengers can get their boarding cards, self-check baggage
bags, and navigate TSA checkpoints and gates without interruption thanks to the
technology.
Only 2% of travelers choose to opt out during the first
launch.
Biometric authentication systems are currently being used by
financial institutions in routine commercial transactions.
They are already widely used to secure personal smart phone
access.
As smart home gadgets linked to the internet need support
for safe financial transactions, intelligent security will become increasingly
more vital.
Opinions on biometrics often shift in response to changing
circumstances and settings.
People who support the use of face recognition technology at
airports to make air travel safer may be opposed to digital fingerprinting at
their bank.
Some individuals believe that private companies' use of
biometric technology dehumanizes them, treating them as goods rather than
persons and following them in real time.
Community policing is often recognized as an effective
technique to create connections between law enforcement personnel and the
communities they police at the local level.
However, other opponents argue that biometric monitoring
shifts the emphasis away from community formation and toward governmental
socio-technical control.
The importance of context, on the other hand, cannot be
overstated.
Biometrics in the workplace may be seen as a leveler, since
it subjects white-collar employees to the same level of scrutiny as blue-collar
workers.
For usage in cloud security systems, researchers are
starting to build video analytics AI software and smart sensors.
In real-time monitoring of workplaces, public spaces, and
residences, these systems can detect known persons, items, sounds, and
movements.
They may also be programmed to warn users when they are in
the presence of strangers.
Artificial intelligence algorithms that were once used to
create biometric systems are now being utilized to thwart them.
GANs, for example, are generative adversarial networks that
replicate human users of network technology and applications.
GANs have been used to build fictitious people's faces using
biometric training data.
GANs are often made up of a creator system that creates each
new picture and a critic system that iteratively compares the fake face to the
original photograph.
In 2020, the firm Icons8 claimed that it could make a
million phony headshots in a single day using just seventy human models.
The firm distributes stock images of the headshots made using
its proprietary StyleGAN technology.
A university, a dating app, and a human resources agency
have all been clients.
Rosebud AI distributes GAN-generated photographs to online
shopping sites and small companies who can't afford to pay pricey models and
photographers.
Deepfake technology has been used to perpetrate hoaxes and
misrepresentations, make fake news clips, and conduct financial fraud.
It uses machine learning algorithms to create convincing but
counterfeit videos.
Facebook profiles with deepfake profile photographs have
been used to boost political campaigns on social media.
Deepfake hacking is possible on smartphones with face
recognition locks.
Deepfake technology may also be used for good.
Such technology has been utilized in films to make
performers seem younger in flashbacks or other similar scenarios.
Digital technology was also employed in films like Rogue
One: A Star Wars Story (2016) to incorporate the late Peter Cushing
(1913–1994), who portrayed the same role from the original 1977 Star Wars
picture.
Face-swapping is available to recreational users via a
number of software apps.
Users may submit a selfie and adjust their hair and facial
expression with FaceApp.
In addition, the computer may mimic the aging of a person's
features.
Zao is a deepfake program that takes a single picture and
replaces the faces of stars from movies and television shows in hundreds of
video.
Deepfake algorithms are now being used to identify the
deepfakes' own videos.
~ Jai Krishna Ponnappan
You may also want to read more about Artificial Intelligence here.
See also:
Biometric Technology.
Further Reading
Goodfellow, Ian J., Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley,
Sherjil Ozair, Aaron Courville, and Yoshua Bengio. 2014. “Generative Adversarial Nets.” NIPS ’14: Proceedings of the 27th International Conference on Neural Information Processing Systems 2 (December): 2672–80.
Hopkins, Richard. 1999. “An Introduction to Biometrics and Large-Scale Civilian Identification.” International Review of Law, Computers & Technology 13, no. 3: 337–63.
Jain, Anil K., Ruud Bolle, and Sharath Pankanti. 1999. Biometrics: Personal Identification in Networked Society. Boston: Kluwer Academic Publishers.
Januškevič, Svetlana N., Patrick S.-P. Wang, Marina L. Gavrilova, Sargur N. Srihari, and Mark S. Nixon. 2007. Image Pattern Recognition: Synthesis and Analysis in Biometrics. Singapore: World Scientific.
Nanavati, Samir, Michael Thieme, and Raj Nanavati. 2002. Biometrics: Identity Verification in a Networked World. New York: Wiley.
Reichert, Ramón, Mathias Fuchs, Pablo Abend, Annika Richterich, and Karin Wenz, eds. 2018. Rethinking AI: Neural Networks, Biometrics and the New Artificial Intelligence. Bielefeld, Germany: Transcript-Verlag.
Woodward, John D., Jr., Nicholas M. Orlans, and Peter T. Higgins. 2001. Biometrics: Identity Assurance in the Information Age. New York: McGraw-Hill.
