At Walt Disney World in Lake Buena Vista, Florida, biometric measurements are taken from the fingers of guests to ensure that a ticket is used by the same person from day to day
Biometrics is the technical term for body measurements and
calculations. It refers to metrics related to human characteristics.
Biometrics authentication (or realistic authentication) is used in
computer science as a form of identification and access control. It is
also used to identify individuals in groups that are under surveillance.
Biometric identifiers are the distinctive, measurable
characteristics used to label and describe individuals. Biometric
identifiers are often categorized as physiological versus behavioral
characteristics. Physiological characteristics are related to the shape
of the body. Examples include, but are not limited to fingerprint, palm veins, facerecognition, DNA, palmprint, handgeometry, irisrecognition,
retina and odour/scent. Behavioral characteristics are related to the
pattern of behavior of a person, including but not limited to typing
rhythm, gait, and voice. Some researchers have coined the term behaviometrics to describe the latter class of biometrics.
More traditional means of access control include token-based identification systems, such as a driver's license or passport,
and knowledge-based identification systems, such as a password or
personal identification number. Since biometric identifiers are unique
to individuals, they are more reliable in verifying identity than token
and knowledge-based methods; however, the collection of biometric
identifiers raises privacy concerns about the ultimate use of this
information.
Biometric functionality
Many different aspects of human physiology, chemistry or behavior can be used for biometric authentication. The selection of a particular biometric for use in a specific application involves a weighting of several factors. Jain et al. (1999) identified seven such factors to be used when assessing the suitability of any trait for use in biometric authentication.
- Universality means that every person using a system should possess the trait.
- Uniqueness means the trait should be sufficiently different for individuals in the relevant population such that they can be distinguished from one another.
- Permanence relates to the manner in which a trait varies over time. More specifically, a trait with 'good' permanence will be reasonably invariant over time with respect to the specific matching algorithm.
- Measurability (collectability) relates to the ease of acquisition or measurement of the trait. In addition, acquired data should be in a form that permits subsequent processing and extraction of the relevant feature sets.
- Performance relates to the accuracy, speed, and robustness of technology used (see performance section for more details).
- Acceptability relates to how well individuals in the relevant population accept the technology such that they are willing to have their biometric trait captured and assessed.
- Circumvention relates to the ease with which a trait might be imitated using an artifact or substitute.
Proper biometric use is very application dependent. Certain
biometrics will be better than others based on the required levels of
convenience and security. No single biometric will meet all the requirements of every possible application.
The block diagram illustrates the two basic modes of a biometric system. First, in verification
(or authentication) mode the system performs a one-to-one comparison of
a captured biometric with a specific template stored in a biometric
database in order to verify the individual is the person they claim to
be. Three steps are involved in the verification of a person.
In the first step, reference models for all the users are generated and
stored in the model database. In the second step, some samples are
matched with reference models to generate the genuine and impostor
scores and calculate the threshold. The third step is the testing step.
This process may use a smart card, username or ID number (e.g. PIN) to indicate which template should be used for comparison.
'Positive recognition' is a common use of the verification mode, "where
the aim is to prevent multiple people from using the same identity".
Second, in identification mode the system performs a one-to-many
comparison against a biometric database in an attempt to establish the
identity of an unknown individual. The system will succeed in
identifying the individual if the comparison of the biometric sample to a
template in the database
falls within a previously set threshold. Identification mode can be
used either for 'positive recognition' (so that the user does not have
to provide any information about the template to be used) or for
'negative recognition' of the person "where the system establishes
whether the person is who she (implicitly or explicitly) denies to be". The latter function can only be achieved through biometrics since other methods of personal recognition such as passwords, PINs or keys are ineffective.
The first time an individual uses a biometric system is called enrollment.
During enrollment, biometric information from an individual is captured
and stored. In subsequent uses, biometric information is detected and
compared with the information stored at the time of enrollment. Note
that it is crucial that storage and retrieval of such systems themselves
be secure if the biometric system is to be robust. Biometric data is
stored and processed with database servers, encrypted tokens,
or physical tokens, while more secure devices will use on-device
storage of biometric templates. This type of storage ensures identity
authentication without the transference of any sensitive biometric
information over the internet to a different server or location.
The first block (sensor) is the interface between the real world and
the system; it has to acquire all the necessary data. Most of the times
it is an image acquisition system, but it can change according to the
characteristics desired. The second block performs all the necessary
pre-processing: it has to remove artifacts from the sensor, to enhance the input (e.g. removing background noise), to use some kind of normalization,
etc. In the third block, necessary features are extracted. This step is
an important step as the correct features need to be extracted in an
optimal way. A vector of numbers or an image with particular properties
is used to create a template. A template is a synthesis of the
relevant characteristics extracted from the source. Elements of the
biometric measurement that are not used in the comparison algorithm are
discarded in the template to reduce the filesize and to protect the
identity of the enrollee.
However, depending on the scope of the biometric system, original
biometric image sources may be retained such as the PIV-cards used in
the Federal Information Processing Standard Personal Identity
Verification (PIV) of Federal Employees and Contractors (FIPS 201).
During the enrollment phase, the template is simply stored
somewhere (on a card or within a database or both). During the matching
phase, the obtained template is passed to a matcher that compares it
with other existing templates, estimating the distance between them
using any algorithm (e.g. Hamming distance).
The matching program will analyze the template with the input. This
will then be output for a specified use or purpose (e.g. entrance in a
restricted area), though it is a fear that the use of biometric data may
face mission creep.
Selection of biometrics in any practical application depending upon the characteristic measurements and user requirements.
In selecting a particular biometric, factors to consider include,
performance, social acceptability, ease of circumvention and/or
spoofing, robustness, population coverage, size of equipment needed and identity theft
deterrence. The selection of a biometric is based on user requirements
and considers sensor and device availability, computational time and
reliability, cost, sensor size, and power consumption.
Multimodal biometric system
Multimodal biometric systems use multiple sensors or biometrics to overcome the limitations of unimodal biometric systems. For instance iris recognition systems can be compromised by aging irises and electronic fingerprint recognition
can be worsened by worn-out or cut fingerprints. While unimodal
biometric systems are limited by the integrity of their identifier, it
is unlikely that several unimodal systems will suffer from identical
limitations. Multimodal biometric systems can obtain sets of information
from the same marker (i.e., multiple images of an iris, or scans of the
same finger) or information from different biometrics (requiring
fingerprint scans and, using voice recognition, a spoken passcode).
Multimodal biometric systems can fuse these unimodal systems
sequentially, simultaneously, a combination thereof, or in series, which
refer to sequential, parallel, hierarchical and serial integration
modes, respectively.
Fusion of the biometrics information can occur at different stages of a
recognition system. In case of feature level fusion, the data itself or
the features extracted from multiple biometrics are fused.
Matching-score level fusion consolidates the scores generated by
multiple classifiers
pertaining to different modalities. Finally, in case of decision level
fusion the final results of multiple classifiers are combined via
techniques such as majority voting.
Feature level fusion is believed to be more effective than the other
levels of fusion because the feature set contains richer information
about the input biometric data than the matching score or the output
decision of a classifier. Therefore, fusion at the feature level is
expected to provide better recognition results.
Spoof attacks
consist in submitting fake biometric traits to biometric systems, and
are a major threat that can curtail their security. Multi-modal
biometric systems are commonly believed to be intrinsically more robust
to spoof attacks, but recent studies have shown that they can be evaded by spoofing even a single biometric trait.
Performance
The following are used as performance metrics for biometric systems:
- False match rate (FMR, also called FAR = False Accept Rate): the probability that the system incorrectly matches the input pattern to a non-matching template in the database. It measures the percent of invalid inputs that are incorrectly accepted. In case of similarity scale, if the person is an imposter in reality, but the matching score is higher than the threshold, then he is treated as genuine. This increases the FMR, which thus also depends upon the threshold value.
- False non-match rate (FNMR, also called FRR = False Reject Rate): the probability that the system fails to detect a match between the input pattern and a matching template in the database. It measures the percent of valid inputs that are incorrectly rejected.
- Receiver operating characteristic or relative operating characteristic (ROC): The ROC plot is a visual characterization of the trade-off between the FMR and the FNMR. In general, the matching algorithm performs a decision based on a threshold that determines how close to a template the input needs to be for it to be considered a match. If the threshold is reduced, there will be fewer false non-matches but more false accepts. Conversely, a higher threshold will reduce the FMR but increase the FNMR. A common variation is the Detection error trade-off (DET), which is obtained using normal deviation scales on both axes. This more linear graph illuminates the differences for higher performances (rarer errors).
- Equal error rate or crossover error rate (EER or CER): the rate at which both acceptance and rejection errors are equal. The value of the EER can be easily obtained from the ROC curve. The EER is a quick way to compare the accuracy of devices with different ROC curves. In general, the device with the lowest EER is the most accurate.
- Failure to enroll rate (FTE or FER): the rate at which attempts to create a template from an input is unsuccessful. This is most commonly caused by low-quality inputs.
- Failure to capture rate (FTC): Within automatic systems, the probability that the system fails to detect a biometric input when presented correctly.
- Template capacity: the maximum number of sets of data that can be stored in the system.
History
An early cataloguing of fingerprints dates back to 1881 when Juan Vucetich started a collection of fingerprints of criminals in Argentina.
Josh Ellenbogen and Nitzan Lebovic argued that Biometrics originated in
the identification systems of criminal activity developed by Alphonse Bertillon (1853–1914) and by Francis Galton's theory of fingerprints and physiognomy.
According to Lebovic, Galton's work "led to the application of
mathematical models to fingerprints, phrenology, and facial
characteristics", as part of "absolute identification" and "a key to
both inclusion and exclusion" of populations. Accordingly, "the biometric system is the absolute political weapon of our era" and a form of "soft control". The theoretician David Lyon
showed that during the past two decades biometric systems have
penetrated the civilian market, and blurred the lines between
governmental forms of control and private corporate control. Kelly A. Gates identified 9/11
as the turning point for the cultural language of our present: "in the
language of cultural studies, the aftermath of 9/11 was a moment of
articulation, where objects or events that have no necessary connection
come together and a new discourse formation is established: automated
facial recognition as a homeland security technology."
Adaptive biometric systems
Adaptive biometric systems aim to auto-update the templates or model to the intra-class variation of the operational data.
The two-fold advantages of these systems are solving the problem of
limited training data and tracking the temporal variations of the input
data through adaptation. Recently, adaptive biometrics have received a
significant attention from the research community. This research
direction is expected to gain momentum because of their key promulgated
advantages. First, with an adaptive biometric system, one no longer
needs to collect a large number of biometric samples during the
enrollment process. Second, it is no longer necessary to enrol again or
retrain the system from scratch in order to cope with the changing
environment. This convenience can significantly reduce the cost of
maintaining a biometric system. Despite these advantages, there are
several open issues involved with these systems. For mis-classification
error (false acceptance) by the biometric system, cause adaptation using
impostor sample. However, continuous research efforts are directed to
resolve the open issues associated to the field of adaptive biometrics.
More information about adaptive biometric systems can be found in the
critical review by Rattani et al.
Recent advances in emerging biometrics
In recent times, biometrics based on brain (electroencephalogram) and heart (electrocardiogram) signals have emerged. The research group at University of Kent led by Ramaswamy Palaniappan has shown that people have certain distinct brain and heart patterns that are specific for each individual. Another example is finger vein recognition,
using pattern-recognition techniques, based on images of human vascular
patterns. The advantage of such 'futuristic' technology is that it is
more fraud resistant compared to conventional biometrics like
fingerprints. However, such technology is generally more cumbersome and
still has issues such as lower accuracy and poor reproducibility over
time.
This new generation of biometrical systems is called biometrics of intent and it aims to scan intent.
The technology will analyze physiological features such as eye
movement, body temperature, breathing etc. and predict dangerous
behaviour or hostile intent before it materializes into action.
On the portability side of biometric products, more and more
vendors are embracing significantly miniaturized biometric
authentication systems (BAS) thereby driving elaborate cost savings,
especially for large-scale deployments.
Operator signatures
An
operator signature is a biometric mode where the manner in which a
person using a device or complex system is recorded as a verification
template. One potential use for this type of biometric signature is to distinguish among remote users of telerobotic surgery systems that utilize public networks for communication.
Proposed requirement for certain public networks
John Michael (Mike) McConnell, a former vice admiral in the United States Navy, a former Director of U.S. National Intelligence, and Senior Vice President of Booz Allen Hamilton
promoted the development of a future capability to require biometric
authentication to access certain public networks in his keynote speech at the 2009 Biometric Consortium Conference.
A basic premise in the above proposal is that the person that has
uniquely authenticated themselves using biometrics with the computer is
in fact also the agent performing potentially malicious actions from
that computer. However, if control of the computer has been subverted,
for example in which the computer is part of a botnet
controlled by a hacker, then knowledge of the identity of the user at
the terminal does not materially improve network security or aid law
enforcement activities.
Recently, another approach to biometric security was developed,
this method scans the entire body of prospects to guarantee a better
identification of this prospect. This method is not globally accepted
because it is very complex and prospects are concerned about their
privacy.
Animal biometrics
Rather
than tags or tattoos, biometric techniques may be used to identify
individual animals: zebra stripes, blood vessel patterns in rodent ears,
muzzle prints, bat wing patterns, primate facial recognition and koala
spots have all been tried.
Video
Videos have
become a pronounced way of identifying information. There are features
in videos that look at how intense certain parts of a frame are compared
to others which help with identification.
Issues and concerns
Surveillance humanitarianism in times of crisis
Biometrics
are employed by many aid programs in times of crisis in order to
prevent fraud and ensure that resources are properly available to those
in need. Humanitarian efforts are motivated by promoting the welfare of
individuals in need, however the use of biometrics as a form of
surveillance humanitarianism can create conflict due to varying
interests of the groups involved in the particular situation. Disputes
over the use of biometrics between aid programs and party officials
stalls the distribution of resources to people that need help the most.
In July of 2019, the United Nations World Food Program and Houthi Rebels
were involved in a large dispute over the use of biometrics to ensure
resources are provided to the hundreds of thousands of civilians in
Yemen whose lives are threatened. The refusal to cooperate with the
interests of the United Nations World Food Program resulted in the
suspension of food aid to the Yemen population. The use of biometrics
may provide aid programs with valuable information, however its
potential solutions may not be best suited for chaotic times of crisis.
Conflicts that are caused by deep-rooted political problems, in which
the implementation of biometrics may not provide a long-term solution.
Human dignity
Biometrics have been considered also instrumental to the development of state authority (to put it in Foucauldian terms, of discipline and biopower). By turning the human subject into a collection of biometric parameters, biometrics would dehumanize the person, infringe bodily integrity, and, ultimately, offend human dignity.
In a well-known case, Italian philosopher Giorgio Agamben
refused to enter the United States in protest at the United States
Visitor and Immigrant Status Indicator (US-VISIT) program's requirement
for visitors to be fingerprinted and photographed. Agamben argued that
gathering of biometric data is a form of bio-political tattooing, akin
to the tattooing of Jews during the Holocaust. According to Agamben,
biometrics turn the human persona into a bare body. Agamben refers to
the two words used by Ancient Greeks for indicating "life", zoe, which is the life common to animals and humans, just life; and bios,
which is life in the human context, with meanings and purposes. Agamben
envisages the reduction to bare bodies for the whole humanity. For him, a new bio-political relationship between citizens and the state is turning citizens into pure biological life (zoe) depriving them from their humanity (bios); and biometrics would herald this new world.
In Dark Matters: On the Surveillance of Blackness, surveillance scholar Simone Browne formulates a similar critique as Agamben, citing a recent study relating to biometrics R&D that found that the gender classification system being researched "is inclined to classify Africans as males and Mongoloids as females."
Consequently, Browne argues that the conception of an objective
biometric technology is difficult if such systems are subjectively
designed, and are vulnerable to cause errors as described in the study
above. The stark expansion of biometric technologies in both the public
and private sector magnifies this concern. The increasing commodification
of biometrics by the private sector adds to this danger of loss of
human value. Indeed, corporations value the biometric characteristics
more than the individuals value them.
Browne goes on to suggest that modern society should incorporate a
"biometric consciousness" that "entails informed public debate around
these technologies and their application, and accountability by the
state and the private sector, where the ownership of and access to one's
own body data and other intellectual property that is generated from
one's body data must be understood as a right."
Other scholars
have emphasized, however, that the globalized world is confronted with a
huge mass of people with weak or absent civil identities. Most
developing countries have weak and unreliable documents and the poorer
people in these countries do not have even those unreliable documents. Without certified personal identities, there is no certainty of right, no civil liberty.
One can claim her rights, including the right to refuse to be
identified, only if she is an identifiable subject, if she has a public
identity. In such a sense, biometrics could play a pivotal role in
supporting and promoting respect for human dignity and fundamental
rights.
The biometrics of intent poses further risks. In his paper in Harvard International Review, Prof Nayef Al-Rodhan
cautions about the high risks of miscalculations, wrongful accusations
and infringements of civil liberties. Critics in the US have also
signalled a conflict with the 4th Amendment.
Privacy and discrimination
It is possible that data obtained during biometric enrollment may be
used in ways for which the enrolled individual has not consented. For
example, most biometric features could disclose physiological and/or
pathological medical conditions (e.g., some fingerprint patterns are
related to chromosomal diseases, iris patterns could reveal genetic sex,
hand vein patterns could reveal vascular diseases, most behavioral
biometrics could reveal neurological diseases, etc.). Moreover, second generation biometrics, notably behavioral and electro-physiologic biometrics (e.g., based on electrocardiography, electroencephalography, electromyography), could be also used for emotion detection.
There are three categories of privacy concerns:
- Unintended functional scope: The authentication goes further than authentication, such as finding a tumor.
- Unintended application scope: The authentication process correctly identifies the subject when the subject did not wish to be identified.
- Covert identification: The subject is identified without seeking identification or authentication, i.e. a subject's face is identified in a crowd.
Danger to owners of secured items
When
thieves cannot get access to secure properties, there is a chance that
the thieves will stalk and assault the property owner to gain access. If
the item is secured with a biometric device, the damage to the owner
could be irreversible, and potentially cost more than the secured
property. For example, in 2005, Malaysian car thieves cut off the finger
of a Mercedes-Benz S-Class owner when attempting to steal the car.
Presentation attacks
In the context of biometric systems, presentation attacks may also be called "spoofing attacks".
As per the recent ISO/IEC 30107 standard,
presentation attacks are defined as "presentation to the biometric
capture subsystem with the goal of interfering with the operation of the
biometric system". These attacks can be either impersonation or
obfuscation attacks. Impersonation attacks try to gain access by
pretending to be someone else. Obfuscation attacks may, for example, try
to evade face detection and face recognition systems.
Recently several methods have been proposed to counteract presentation attacks. Today's sophisticated biometric systems use "liveness" elements to detect spoofs (a.k.a. fake images), and some fingerprint scanners have pulse detectors. Automated detection of a presentation attack is called a "presentation attack detection" (PAD).
Cancelable biometrics
One
advantage of passwords over biometrics is that they can be re-issued.
If a token or a password is lost or stolen, it can be cancelled and
replaced by a newer version. This is not naturally available in
biometrics. If someone's face is compromised from a database, they
cannot cancel or reissue it. If the electronic biometric identifier is
stolen, it is nearly impossible to change a biometric feature. This
renders the person's biometric feature questionable for future use in
authentication, such as the case with the hacking of
security-clearance-related background information from the Office of
Personnel Management (OPM) in the United States.
Cancelable biometrics is a way in which to incorporate protection
and the replacement features into biometrics to create a more secure
system. It was first proposed by Ratha et al.
"Cancelable biometrics refers to the intentional and
systematically repeatable distortion of biometric features in order to
protect sensitive user-specific data. If a cancelable feature is
compromised, the distortion characteristics are changed, and the same
biometrics is mapped to a new template, which is used subsequently.
Cancelable biometrics is one of the major categories for biometric
template protection purpose besides biometric cryptosystem." In biometric cryptosystem, "the error-correcting coding techniques are employed to handle intraclass variations." This ensures a high level of security but has limitations such as specific input format of only small intraclass variations.
Several methods for generating new exclusive biometrics have been
proposed. The first fingerprint-based cancelable biometric system was
designed and developed by Tulyakov et al.
Essentially, cancelable biometrics perform a distortion of the
biometric image or features before matching. The variability in the
distortion parameters provides the cancelable nature of the scheme. Some
of the proposed techniques operate using their own recognition engines,
such as Teoh et al. and Savvides et al., whereas other methods, such as Dabbah et al.,
take the advantage of the advancement of the well-established biometric
research for their recognition front-end to conduct recognition.
Although this increases the restrictions on the protection system, it
makes the cancellable templates more accessible for available biometric
technologies
Soft biometrics
Soft biometrics
traits are physical, behavioral or adhered human characteristics that
have been derived from the way human beings normally distinguish their
peers (e.g. height, gender, hair color). They are used to complement the
identity information provided by the primary biometric identifiers.
Although soft biometric characteristics lack the distinctiveness and
permanence to recognize an individual uniquely and reliably, and can be
easily faked, they provide some evidence about the users identity that
could be beneficial. In other words, despite the fact they are unable to
individualize a subject, they are effective in distinguishing between
people. Combinations of personal attributes like gender, race, eye
color, height and other visible identification marks can be used to
improve the performance of traditional biometric systems.
Most soft biometrics can be easily collected and are actually collected
during enrollment. Two main ethical issues are raised by soft
biometrics.
First, some of soft biometric traits are strongly cultural based; e.g.,
skin colors for determining ethnicity risk to support racist
approaches, biometric sex recognition at the best recognizes gender from
tertiary sexual characters, being unable to determine genetic and
chromosomal sexes; soft biometrics for aging recognition are often
deeply influenced by ageist stereotypes, etc. Second, soft biometrics
have strong potential for categorizing and profiling people, so risking
of supporting processes of stigmatization and exclusion.
International sharing of biometric data
Many countries, including the United States, are planning to share biometric data with other nations.
In testimony before the US House Appropriations Committee,
Subcommittee on Homeland Security on "biometric identification" in 2009,
Kathleen Kraninger and Robert A Mocny commented on international cooperation and collaboration with respect to biometric data, as follows:
To ensure we can shut down terrorist networks before they ever get to the United States, we must also take the lead in driving international biometric standards. By developing compatible systems, we will be able to securely share terrorist information internationally to bolster our defenses. Just as we are improving the way we collaborate within the U.S. Government to identify and weed out terrorists and other dangerous people, we have the same obligation to work with our partners abroad to prevent terrorists from making any move undetected. Biometrics provide a new way to bring terrorists' true identities to light, stripping them of their greatest advantage—remaining unknown.
According to an article written in 2009 by S. Magnuson in the
National Defense Magazine entitled "Defense Department Under Pressure to
Share Biometric Data" the United States has bilateral agreements with
other nations aimed at sharing biometric data. To quote that article:
Miller [a consultant to the Office of Homeland Defense and America's security affairs] said the United States has bilateral agreements to share biometric data with about 25 countries. Every time a foreign leader has visited Washington during the last few years, the State Department has made sure they sign such an agreement.
Likelihood of full governmental disclosure
Certain
members of the civilian community are worried about how biometric data
is used but full disclosure may not be forthcoming. In particular, the
Unclassified Report of the United States' Defense Science Board Task
Force on Defense Biometrics states that it is wise to protect, and
sometimes even to disguise, the true and total extent of national
capabilities in areas related directly to the conduct of
security-related activities.
This also potentially applies to Biometrics. It goes on to say that
this is a classic feature of intelligence and military operations. In
short, the goal is to preserve the security of 'sources and methods'.
Countries applying biometrics
Countries using biometrics include Australia, Brazil, Canada, Cyprus, Greece, China, Gambia, Germany, India, Iraq, Ireland, Israel, Italy, Malaysia, Netherlands, New Zealand, Nigeria, Norway, Pakistan, South Africa, Saudi Arabia, Tanzania, Ukraine, United Arab Emirates, United Kingdom, United States and Venezuela.
Among low to middle income countries, roughly 1.2 billion people
have already received identification through a biometric identification
program.
There are also numerous countries applying biometrics for voter registration and similar electoral purposes. According to the International IDEA's ICTs in Elections Database, some of the countries using (2017) Biometric Voter Registration (BVR) are Armenia, Angola, Bangladesh, Bhutan, Bolivia, Brazil, Burkina Faso, Cambodia, Cameroon, Chad, Colombia, Comoros, Congo (Democratic Republic of), Costa Rica, Ivory Coast, Dominican Republic, Fiji, Gambia, Ghana, Guatemala, India, Iraq, Kenya, Lesotho, Liberia, Malawi, Mali, Mauritania, Mexico, Morocco, Mozambique, Namibia, Nepal, Nicaragua, Nigeria, Panama, Peru, The Philippines, Senegal, Sierra Leone, Solomon Islands, Somaliland, Swaziland, Tanzania, Uganda, Uruguay, Venezuela, Yemen, Zambia, and Zimbabwe.
India's national ID program
India's national ID program called Aadhaar
is the largest biometric database in the world. It is a
biometrics-based digital identity assigned for a person's lifetime,
verifiable
online instantly in the public domain, at any time, from anywhere, in a
paperless way. It is designed to enable government agencies to deliver a
retail public service, securely based on biometric data (fingerprint, iris scan and face photo), along with demographic data (name, age, gender, address, parent/spouse name, mobile phone number)
of a person. The data is transmitted in encrypted form over the
internet for authentication, aiming to free it from the limitations of
physical presence of a person at a given place.
About 550 million residents have been enrolled and assigned 480 million Aadhaar national identification numbers as of 7 November 2013. It aims to cover the entire population of 1.2 billion in a few years.
However, it is being challenged by critics over privacy concerns and
possible transformation of the state into a surveillance state, or into a
Banana republic.§ The project was also met with mistrust regarding the safety of the social protection infrastructures.
To tackle the fear amongst the people, India's supreme court put a new
ruling into action that stated that privacy from then on was seen as a
fundamental right. On 24 August 2017 this new law was established.
