Modalities

Commonly implemented or studied biometric modalities include fingerprint, face, iris, voice, hand writer recognition, and hand geometry. Many other modalities are in various stages of development and assessment. There is not one biometric modality that is best for all implementations, and many factors must be taken into account when implementing a biometric device, including location, security risks, task (identification or verification), expected number of users, user circumstances, existing data, etc. It is also important to note that biometric modalities are in varying stages of maturity.

Facial Recognition 

Introduction

Facial Recognition

Humans often use faces to recognize individuals, and advancements in computing capability over the past few decades now enable similar recognitions automatically. Early facial recognition algorithms used simple geometric models, but the recognition process has now matured into a science of sophisticated mathematical representations and matching processes. Major advancements and initiatives in the past 10 to 15 years have propelled facial recognition technology into the spotlight. Facial recognition can be used for both verification and identification (open-set and closed-set).

History

Automated facial recognition is a relatively new concept. Developed in the 1960s, the first semi-automated system for facial recognition required the administrator to locate features (such as eyes, ears, nose, and mouth) on photographs before it calculated distances and ratios to a common reference point, which were then compared to reference data. More information (pdf)

Predominant Approaches

There are two predominant approaches to the facial recognition problem: geometric (feature based) and photometric (view based). More information (pdf)

Standards Overview

Standardization is a vital portion of the advancement of the market and state-of-the-art. Much work is being done at the national and international standard organization levels to facilitate the interoperability and data interchange formats, which will help facilitate technology improvement on a standardized platform. More information (pdf)

Summary

The computer-based facial recognition industry has made many useful advancements in the past decade; however, the need for higher accuracy remains. Through the determination and commitment of industry, government evaluations, and organized standards bodies, growth and progress will continue, raising the bar for face-recognition technology.

Note: The text above is excerpted from biometrics.gov.


Palm Print 

Palm PrintIntroduction

Palm print recognition inherently implements many of the same matching characteristics that have allowed fingerprint recognition to be one of the most well-known and best publicized biometrics. Both palm and finger biometrics are represented by the information presented in a friction ridge impression. This information combines ridge flow, ridge characteristics, and ridge structure of the raised portion of the epidermis. The data represented by these friction ridge impressions allows a determination that corresponding areas of friction ridge impressions either originated from the same source or could not have been made by the same source. Because fingerprints and palms have both uniqueness and permanence, they have been used for more than a century as a trusted form of identification. However, palm recognition has been slower in becoming automated due to some restraints in computing capabilities and live-scan technologies.

History

In many instances throughout history, examination of handprints was the only method of distinguishing one illiterate person from another since they could not write their names. More information (pdf)

Approach Concept

Palm identification, just like fingerprint identification, is based on the aggregate of information presented in a friction ridge impression. More information (pdf)

Hardware

A variety of sensor types—capacitive, optical, ultrasound, and thermal—can be used for collecting the digital image of a palm surface. More information (pdf)

Software

Some palm recognition systems scan the entire palm, while others require the palms to be segmented into smaller areas to optimize performance. More information (pdf)

United States Government Evaluations

Unlike several other biometrics, a large-scale government-sponsored evaluation has not been performed for palm recognition. More information (pdf)

Standards Overview

Just as with fingerprints, standards development is an essential element in palm recognition because of the vast variety of algorithms and sensors available on the market. More information (pdf)

Summary

Even though total error rates are decreasing when comparing live-scan enrollment data with live-scan verification data, improvements in matches between live-scan and latent-print data are still needed. Data indicates that fully integrated palm- and fingerprint multi-biometric systems are widely used for identification and verification of criminal subjects as well as in security access applications. However, there are still significant challenges in balancing accuracy with system cost. Image matching accuracy may be improved by building and using larger databases and by employing more processing power, but then purchase and maintenance costs will most certainly rise as the systems become larger and more sophisticated. Future challenges require balancing the need for more processing power with more improvement in algorithm technology to produce affordable systems to all levels of law enforcement.

Note: The text above is excerpted from biometrics.gov.


Iris Scan 

Iris ScanIntroduction

Iris recognition is the process of recognizing a person by analyzing the random pattern of the iris. The automated method of iris recognition is relatively young, existing in patent since only 1994. The iris is a muscle within the eye that regulates the size of the pupil, controlling the amount of light that enters the eye. It is the colored portion of the eye, and the coloring is based on the amount of melatonin pigment within the muscle. Although the coloration and structure of the iris are genetically linked, the pattern details are not. The iris develops during prenatal growth through a process of tight forming and folding of the tissue membrane. Prior to birth, degeneration occurs, resulting in the pupil opening and the iris forming random, unique patterns. Although genetically identical, an individual’s irides are unique and structurally distinct, which allows for them to be used for recognition purposes.

History

In 1936, ophthalmologist Frank Burch proposed the concept of using iris patterns as a method to recognize an individual. More information (pdf)

Approach

Before recognition of the iris takes place, the iris is located using landmark features. These landmark features and the iris’ distinct shape allow for imaging, feature isolation, and extraction. More information (pdf)

Iris vs. Retina Recognition

While iris recognition utilizes the iris muscle to perform verification, retinal recognition uses the unique pattern of blood vessels on an individual’s retina at the back of the eye. More information (pdf)

United States Government Evaluations

The U.S. Department of Homeland Security and the Intelligence Technology Innovation Center co-sponsored a test of iris recognition accuracy, usability, and interoperability referred to as the Independent Testing of Iris Recognition Technology. More information (pdf)

Standards Overview

Current standards work in the area of iris recognition exists on the national and international level. More information (pdf)

Summary

Having only become automated and available within the past decade, the iris recognition concept and industry are still relatively new, so a need for continued research and testing remains. Through the determination and commitment of industry, government evaluations, and organized standards bodies, growth, and progress will continue to raise the bar for recognition technology.

Note: The text above is excerpted from biometrics.gov.


Voice Recognition 


Voice RecognitionIntroduction

Speaker, or voice, recognition is a biometric modality that uses an individual’s voice for recognition purposes. It is a different technology than “speech recognition,” which recognizes words as they are articulated, which is not a biometric. The speaker recognition process relies on features influenced by both the physical structure of an individual’s vocal tract and the individual’s behavioral characteristics.

A popular choice for remote authentication due to the availability of devices for collecting speech samples (e.g., telephone network and computer microphones) and its ease of integration, speaker recognition is different from some other biometric methods in that speech samples are captured dynamically or over a period of time, such as a few seconds. Analysis occurs on a model in which changes over time are monitored, which is similar to other behavioral biometrics such as dynamic signature, gait, and keystroke recognition.

History

Speaker verification has co-evolved with the technologies of speech recognition and speech synthesis because of similar characteristics and challenges associated with each. More information (pdf)

Approach

The physiological component of voice recognition is related to the physical shape of an individual’s vocal tract, which consists of an airway and the soft tissue cavities from which vocal sounds originate. More information (pdf)

United States Government Evaluations

Since 1996, the National Institute of Standards and Technology (NIST) has been conducting an ongoing series of yearly evaluations called the “NIST Speaker Recognition Evaluations.” More information (pdf)

Standards Overview

Standards play an important role in the development and sustainability of technology, and work in the international and national standards arena will facilitate the improvement of biometrics. The major standards work in the area of speaker recognition involves the Speaker Verification Application Program Interface, or SVAPI, which is used by technology developers and allows for compatibility and interoperability between various vendors and networks. More information (pdf)

Summary

Thanks to the commitment of researchers and the support of the National Security Agency and NIST, voice recognition will continue to evolve as communication and computing technology advance. Their determination will help further develop the technology into a reliable and consistent means of identification for use in remote recognition.

Note: The text above is excerpted from biometrics.gov.


Fingerprint 

FingerprintIntroduction

Fingerprint identification is one of the most well-known and publicized biometrics. Because of their uniqueness and consistency over time, fingerprints have been used for identification for more than a century, more recently becoming automated (i.e., a biometric) due to advancements in computing capabilities. Fingerprint identification is popular because of the inherent ease in acquisition, the numerous sources (10 fingers) available for collection, and their established use and collections by law enforcement and immigration.

History

The practice of using fingerprints as a method of identifying individuals has been in use since the late 19th century when Sir Francis Galton defined some of the points or characteristics from which fingerprints can be identified. More information (pdf)

Approach

Concept

A fingerprint usually appears as a series of dark lines that represent the high, peaking portion of the friction ridge skin, while the valleys between these ridges appears as white space and are the low, shallow portion of the friction ridge skin. More information (pdf)

Hardware

A variety of sensor types — optical, capacitive, ultrasound, and thermal — are used for collecting the digital image of a fingerprint surface. More information (pdf)

Software

The two main categories of fingerprint matching techniques are minutiae-based matching and pattern matching. More information (pdf)

United States Government Evaluations

As mandated by the Uniting and Strengthening America by Providing Appropriate Tools Required to Intercept and Obstruct Terrorism (USA Patriot) Act and the Enhanced Border Security Act, National Institute of Standards and Technology (NIST) managed the Fingerprint Vendor Technology Evaluation to evaluate the accuracy of fingerprint recognition systems. More information (pdf)

Standards Overview

Currently ongoing at the national and international levels, fingerprints standards development is an essential element in fingerprint recognition because of the vast variety of algorithms and sensors available on the market. More information (pdf)

Notable U.S. Government Fingerprint Programs

Fast Capture of Rolled-Equivalent Fingerprints and Palmprints

Fast Capture, a multi-agency government initiative, is expanding fingerprint and palm research, challenging industry to develop and demonstrate technology to capture 10 rolled-equivalent fingerprints in less than 15 seconds and/or both palm prints in less than one minute. More information (pdf)

Integrated Automatic Fingerprint Identification System (IAFIS)

Maintained by the FBI Criminal Justice Information Services (CJIS) Division, IAFIS contains more than 61 million subjects. System capabilities include automated tenprint and latent fingerprint searches, electronic image storage, and electronic exchanges of fingerprints and responses. More information (pdf)

NIST Special Publication 800-76

NIST Special Publication 800-76, “Biometric Data Specification for Personal Identity Verification,” contains specifics for acquiring, formatting, and storing fingerprint images; templates for collecting and formatting facial images; and specifications for biometric devices used to collect and read fingerprint images. More information (pdf)

United States Visitor and Immigrant Status Indicator Technology (US-VISIT)

The US-VISIT program is the centerpiece of the United States government’s efforts to transform our nation’s border management and immigration systems in a way that meets the needs and challenges of the 21st century. More information (pdf)

Summary

For more than a century, fingerprints have been one of the most highly used methods for human recognition; automated biometric systems have only been available in recent years. The determination and commitment of the fingerprint industry, government evaluations and needs, and organized standards bodies have led to the next generation of fingerprint recognition, which promises faster and higher quality acquisition devices to produce higher accuracy and more reliability. Because fingerprints have a generally broad acceptance with the general public, law enforcement, and the forensic science community, they will continue to be used with many governments’ legacy systems and will be utilized in new systems for evolving applications that require a reliable biometric.

Note: The text above is excerpted from biometrics.gov.


DNA 

DNAIntroduction

Genes make up 5 percent of the human genome. The other 95 percent are non-coding sequences, which used to be called junk DNA. In non-coding regions there are identical repeat sequences of DNA, which can be repeated anywhere from one to 30 times in a row. These regions are called variable number tandem repeats (VNTRs). The number of tandem repeats at specific places, called loci, on chromosomes varies between individuals. For any given VNTR loci in an individual’s DNA, there will be a certain number of repeats. The higher the number of loci that are analyzed, the smaller the probability to find two unrelated individuals with the same DNA profile.

DNA profiling determines the number of VNTR repeats at a number of distinctive loci and uses it to create an individual’s DNA profile. The main steps to create a DNA profile are: isolate the DNA from a sample such as blood, saliva, hair, semen, or tissue, cut the DNA up into shorter fragments containing known VNTR areas, sort the DNA fragments by size, and compare the DNA fragments in different samples.

The benefit of using DNA as a biometric identifier is the level of accuracy offered: the chance of two individuals sharing the same DNA profile is less than one in a 100 billion with 26 different bands studied.


Emerging Biometrics 

Emerging BiometricsIntroduction

The FBI Biometric Center of Excellence (BCOE) will be leveraging the potential of newly emerging biometric technology to allow federal government agencies to increase their identity management capabilities. The BCOE will assist in implementing newly developed biometric modalities such as facial recognition, iris recognition, and palm print matching into large-scale federal government biometric systems. Research will be performed to support the multimodal fusion of numerous biometrics to result in a significantly more accurate and comprehensive identity management system. The BCOE will also work on developing and enhancing other potential new biometric technologies including footprint and hand geometry, gait recognition, etc.