Identity screening systems

“True” biometric systems began to emerge in the latter half of the twentieth century, coinciding with the emergence of computer systems, and incorporating existing forensics. The field experienced an explosion of activity in the 1990s and began to surface in everyday applications in the early 2000s. Any human physiological and/or behavioral characteristic can be used as a biometric characteristic as long as it satisfies the following requirements:

  • Universality: each person should have the characteristic.
  • Distinctiveness: any two persons should be sufficiently different in terms of the characteristic.
  • Permanence: the characteristic should be sufficiently invariant (with respect to the matching criterion) over a period of time.
  • Collectability: the characteristic can be measured quantitatively.

In a practical biometric system (a system that employs biometrics for personal recognition), there are a number of other issues that are considered:

  • Performance: referring to the achievable recognition accuracy and speed, the resources required to achieve the desired recognition accuracy and speed, as well as the operational and environmental factors that affect the accuracy and speed
  • Acceptability: an indicator of the extent to which people are willing to accept the use of a particular biometric identifier (characteristic) in their daily lives
  • Circumvention: reflecting how easily the system can be fooled using fraudulent methods

Systemic descriptions are from An Introduction to Biometric Recognition. Most (but not all) databases listed below are merely intended for research and as such indicators on current development state and direction of the biometrics (research) field.

Facial recognition

One of the oldest and most basic examples of a characteristic that is used for recognition is the face. Since the beginning of civilization, humans have used faces to identify known (familiar) and unknown (unfamiliar) individuals.

Face recognition is a nonintrusive method, and facial images are probably the most common biometric characteristic used by humans to make a personal recognition. The applications of facial recognition range from a static, controlled “mug-shot” verification to a dynamic, uncontrolled face identification in a cluttered background (airport). The most popular approaches to face recognition are based on either:

  1. the location and shape of facial attributes such as the eyes, eyebrows, nose, lips and chin, and their spatial relationships, or
  2. the overall (global) analysis of the face image that represents a face as a weighted combination of a number of canonical faces.

While the verification performance of the face recognition systems that are commercially available is reasonable, they impose a number of restrictions on how the facial images are obtained, sometimes requiring a fixed and simple background or special illumination. These systems also have difficulty in recognizing a face from images captured from two drastically different views and under different illumination conditions. It is questionable whether the face itself, without any contextual information, is a sufficient basis for recognizing a person from a large number of identities with an extremely high level of confidence. In order for a facial recognition system to work well in practice, it should automatically:

  1. detect whether a face is present in the acquired image;
  2. locate the face if there is one; and
  3. recognize the face from a general viewpoint (from any pose).

Facial recognition can be used not just to identify an individual, but also to unearth other personal data associated with an individual (photos featuring the individual, blog posts, social networking profiles, digital behavior, travel patterns) all through facial features alone.

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The newest research databases indicate the field is working on performance improvements for recognising faces from a general viewpoint (from any pose).

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Fingerprints were used as a person’s mark as early as 2000 BC during the building of the pyramids in Egypt. In 500 BC, Babylonian business transactions are recorded in clay tablets that include fingerprints. Early Chinese merchants used fingerprints to settle business transactions. Chinese parents also used fingerprints and footprints to differentiate children from one another. Humans have used fingerprints for personal identification for many centuries and the matching accuracy using fingerprints has been shown to be very high.

A fingerprint is the pattern of ridges and valleys on the surface of a fingertip, the formation of which is determined during the first seven months of fetal development. Fingerprints of identical twins are different and so are the prints on each finger of the same person. Today, a fingerprint scanner costs next to nothing when ordered in large quantities and the marginal cost of embedding a fingerprint-based biometric in a system (laptop computer) has become affordable in a large number of applications. The accuracy of the currently available fingerprint recognition systems is adequate for verification systems and small- to medium-scale identification systems involving a few hundred users. Multiple fingerprints of a person provide additional information to allow for large-scale recognition involving millions of identities. One problem with the current fingerprint recognition systems is that they require a large amount of computational resources, especially when operating in the identification mode. Finally, fingerprints of a small fraction of the population may be unsuitable for automatic identification because of genetic factors, aging, environmental, or occupational reasons (manual workers may have a large number of cuts and bruises on their fingerprints that keep changing).

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Interpol provides authorized users in member countries with view, submit and cross-check records in the fingerprints database via a user-friendly automatic fingerprint identification system (AFIS).

The FBI’s Criminal Justice Information Services (CJIS) Division operated and maintained IAFIS, which became the world’s largest person-centric biometric database when it was implemented in July 1999.

AFIT, part of NGI, deployed on February 25, 2011, enhances fingerprint and latent processing services, increases the accuracy and daily fingerprint processing capacity, and improves system availability. This deployment implemented a new fingerprint matching algorithm which has improved matching accuracy from approximately 92% to over 99.6%.

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Gait recognition

Gait is the peculiar way one walks and is a complex spatio-temporal biometric. Gait is not supposed to be very distinctive, but is sufficiently discriminatory to allow verification in some low-security applications. Gait is a behavioral biometric and may not remain invariant, especially over a long period of time, due to fluctuations in body weight, major injuries involving joints or brain, or due to inebriety. Acquisition of gait is similar to acquiring a facial picture and, hence, may be an acceptable biometric. Since gait-based systems use the video-sequence footage of a walking person to measure several different movements of each articulate joint, it is input intensive and computationally expensive.
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Relatively recent additions to research databases seem focused on exploiting the relations between behaviour biometrics and its corresponding prints.

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Voice recognition

Voice is a combination of physiological and behavioral biometrics. The features of an individual’s voice are based on the shape and size of the appendages (vocal tracts, mouth, nasal cavities, and lips) that are used in the synthesis of the sound. These physiological characteristics of human speech are invariant for an individual, but the behavioral part of the speech of a person changes over time due to age, medical conditions (such as a common cold), and emotional state, etc. Voice is also not very distinctive and may not be appropriate for large-scale identification. A text-dependent voice recognition system is based on the utterance of a fixed predetermined phrase. A text-independent voice recognition system recognizes the speaker independent of what she speaks. A text-independent system is more difficult to design than a text-dependent system but offers more protection against fraud. A disadvantage of voice-based recognition is that speech features are sensitive to a number of factors such as background noise. Speaker recognition is most appropriate in phone-based applications but the voice signal over phone is typically degraded in quality by the microphone and the communication channel.
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The main characteristics of databases before 2005: English spoken by non-native speakers, sessions of sentence reading and relatively extensive speech samples suitable for learning person specific speech characteristics. The PRISM set is a more recent database definition for evaluation of speaker recognition systems. The database is created using data from NIST SREs from 2005 to 2010, as well as other data sets available through LDC. This database involves types of variability already seen in NIST speaker recognition evaluations (SREs) like language, channel, speech style and vocal effort, and new types not yet available on any standard database like severe noise, and reverberation.

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Iris scans

The iris is the annular region of the eye bounded by the pupil and the sclera (white of the eye) on either side. The visual texture of the iris is formed during fetal development and stabilizes during the first two years of life. The complex iris texture carries very distinctive information useful for personal recognition. The accuracy and speed of currently deployed iris-based recognition systems is promising and point to the feasibility of large-scale identification systems based on iris information. Each iris is distinctive and, like fingerprints, even the irises of identical twins are different. It is extremely difficult to surgically tamper the texture of the iris. Further, it is rather easy to detect artificial irises (designer contact lenses). Although, the early iris-based recognition systems required considerable user participation and were expensive, the newer systems have become more user-friendly and cost-effective.
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As the iris of the eye gains momentum as a strong biometric capability, IR is poised to offer law enforcement a new tool to determine identity. The NGI iris pilot evaluates the technology in an operational setting.

Automatic iris recognition has to face unpredictable variations of iris images in real-world applications. For example, recognition of iris images of poor quality, nonlinearly deformed iris images, iris images at a distance, iris images on the move, and faked iris images, all are open problems in iris recognition.

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DNA fingerprints

Deoxyribonucleic acid (DNA) is the one-dimensional (1D) ultimate unique code for one’s individuality — except for the fact that identical twins have identical DNA patterns. It is, however, currently used mostly in the context of forensic applications for person recognition. Three issues limit the utility of this biometrics for other applications:

  1. contamination and sensitivity: it is easy to steal a piece of DNA from an unsuspecting subject that can be subsequently abused for an ulterior purpose;
  2. automatic real-time recognition issues: the present technology for DNA matching requires cumbersome chemical methods (wet processes) involving an expert’ s skills and is not geared for on-line noninvasive recognition; and
  3. privacy issues: information about susceptibilities of a person to certain diseases could be gained from the DNA pattern and there is a concern that the unintended abuse of genetic code information may result in discrimination, for example in hiring practices.
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Due to the overwhelming success of DNA databases, a political process was initiated by a number of European countries to establish a legal basis for exchanging DNA database profiles between countries in criminal investigations. This led to the Treaty of Prüm, which was signed in 2005 with the purpose of “stepping up cross-border cooperation, particularly in combating terrorism, cross-border crime and illegal migration”.

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The palms of the human hands contain pattern of ridges and valleys much like the fingerprints. The area of the palm is much larger than the area of a finger and, as a result, palmprints are expected to be even more distinctive than the fingerprints. Since palmprint scanners need to capture a large area, they are bulkier and more expensive than the fingerprint sensors. Human palms also contain additional distinctive features such as principal lines and wrinkles that can be captured even with a lower resolution scanner, which would be cheaper. Finally, when using a high-resolution palmprint scanner, all the features of the palm such as hand geometry, ridge and valley features (minutiae and singular points such as deltas), principal lines, and wrinkles may be combined to build a highly accurate biometric system.
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In 2013, the NGI System deployed the new NPPS which contains millions of palm prints that are now searchable on a nationwide basis. The NPPS and improvements in latent fingerprint search performance are providing powerful new and enhanced crime-solving capabilities for more than 18,000 local, state, tribal, and federal law enforcement agencies across the US.

The research databases indicate upcoming developments in combing multiple imaging modalities to enhance performance of recognition.

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Digital fingerprints

One of the hallmarks of cutting-edge cybersurveillance is that it can be conducted remotely and automatically, virtually and near invisibly, constantly and near costlessly. In recent years, digital fingerprinting has been used to describe a method of identity tracking combining details (IP address, login identity used on multiple sites, operating system installed, web browser and version of it used) to add to a fingerprint.

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And, forensic digital fingerprinting is not just about associating metadata … It is hypothesized that each person types on a keyboard in a characteristic way. This behavioral biometric is not expected to be unique to each individual but it offers sufficient discriminatory information to permit identity verification. Keystroke dynamics is a behavioral biometric; for some individuals, one may expect to observe large variations in typical typing patterns. Further, the keystrokes of a person using a system could be monitored unobtrusively as that person is keying in information.

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In the United States, the NSA is collecting the phone records of more than 300 million Americans. The international surveillance tool XKeyscore allows government analysts to search through vast databases containing emails, online chats and the browsing histories of millions of individuals.Britain’s global surveillance program Tempora intercepts the fibre-optic cables that form the backbone of the Internet. Under the NSA’s PRISM surveillance program, data that has already reached its final destination would be directly harvested from the servers of the following US service providers: Microsoft, Yahoo!, Google, Facebook, Paltalk, AOL, Skype, YouTube, and Apple Inc.

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Linguistic fingerprinting

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Multimodal screening

Multimodal biometric systems address the problem of nonuniversality, since multiple traits ensure sufficient population coverage. Used for verification, multimodal biometric systems provide antispoofing measures by making it difficult for an intruder to simultaneously spoof the multiple biometric traits of a legitimate user. For identification, multimodal biometric identification systems combine forms of recognition characteristics to increase the reliability of identity screening systems.

In the US, an era in biometric identification and investigation came to a close and a new one began on September 7, 2014, when the FBI’s Criminal Justice Information Services (CJIS) Division officially decommissioned the 15-year-old Integrated Automated Fingerprint Identification System (IAFIS) that first went online in July 1999, and, in turn, deployed the Next Generation Identification (NGI) system. The new system is being rolled out in seven increments, each bringing a new level of functionality to storing and searching biometric criminal records. With Increment 3, the system is now more than 70 percent deployed.

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Monkey business

Ah yes, Big Business. Of course. The complete works of William Shakespeare. And a kazillion people can have bullshit jobs.

“With a struggling economy, the opportunity for innovation and economic growth is explosive. The global biometrics market was $7 billion in 2012. By 2020, it is slated to grow to $20 billion,” said SIBA founder and CEO Janice Kephart. “With policies in place that embrace responsible future applications, these technologies can evolve into a core stabilizer for the economy’s recovery and future growth.”

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Legitimacy & coercion

More and more US and EU policy experts and lobbyists are calling for multimodal biometric identification systems but the surveillance consequences of such programs and protocols are obscured because they are implemented in a manner that may appear to be reasonable (ID cards) and expected (identity or citizenship status verification protocols).

In the aftermath of the Snowden revelations in the United States, it was reported that a number of EU Members, including France, Germany, Sweden, and the United Kingdom, were allegedly involved in mass surveillance operations in cooperation with the United States. At the EU level, large-scale surveillance conducted by government agencies of the EU Member States has raised concerns as to the compatibility of such activities with human rights standards. The Parliament’s Resolution on the US NSA Surveillance Programme, Surveillance Bodies in Various EU Members States and Their Impact on EU Citizens’ Fundamental Rights has value as a political statement, but it lacks binding force.

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The surveillance consequences are also obscured because these methodologies may appear on face to be consensual (voluntarily submitting to Internet database-screening protocols which, in turn, permits the harvesting, aggregation, and analysis of identity data). The manner in which personally identifiable data is shared by people with governments or other third parties may not appear to implicate traditional privacy concerns (employer identity database screening of employees as directed by law as a precondition for hiring) and may even be coerced (as seems to be the case in India):

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Biometrics in general, in particular identity tracking, treads on shaky ethical ground that may be deemed overly invasive and unlawful in the future. But because it is developing technology, the legal issues are still being sorted out. And with the internet being a global network (driven by concealed interests of the war-work-machine), laws regarding digital fingerprinting may develop completely differently from one country to another regardless of raised concerns at the EU level as to the compatibility of such activities with human rights standards.

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Biometrics’ biggest risk to privacy comes from the ability to use it for surveillance so I don’t expect governments to protect private data of people any time soon. Not only do governments directly benefit from such arrogation and appropriation for control over their own population and for serving its “military allies” in teh world domination endgame, governments (politicians) indirectly benefit from any economic growth and receive economic pressure and messages (and maybe even a part of their paychecks) from the biometrics lobby (in some cases supported and funded by military intelligence interests). And following the money, some of these economic allies happen to own a huge big cook-the-frog-slowly media circus machine lubing reversal of ownership (in this case of our personal data).


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Privacy concerns

As identity recognition technologies become more effective and devices are capable of recording greater detail from multiple sensoring modalities, identification and tracking will become the norm.

The problems are multiplied when biometrics databases are “multimodal”, allowing the collection and storage of several different biometrics in one database and combining them with other data(bases). Association with simple traditional data points like name, address, social security number, gender, race, and date of birth are already devastating to our privacy.

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Application news

Meanwhile the machine rolls on:

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Monitoring the development and implementation of biometric technologies in law enforcement and commercial contexts:

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8yk7rTime to have some fun with (unexpected) humorous moves.

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