`
`Introduction
`
`This set of terms was developed by the National Science &
`Technology Council’s (NSTC) Subcommittee on Biometrics with the
`full understanding that national (INCITS/M1) and international
`(ISO/IEC JTC1 SC37) standards bodies are working to develop
`standard references. The subcommittee will review this Glossary
`for consistency as standards are passed. The subcommittee
`recognizes the impact of ongoing challenge problems, technical
`evaluations, and technology advancements. The Glossary will be
`updated accordingly to reflect these changes. The statements
`herein are intended to further the understanding of a general
`audience and are not intended to replace or compete with
`sources that may be more technically descriptive/prescriptive.
`
`Glossary Terms
`
`Accuracy
`A catch-all phrase for describing how well a biometric system
`performs. The actual statistic for performance will vary by task
`(verification, open-set identification (watchlist), and closed-set
`identification). See
`www.biometricscatalog.org/biometrics/biometrics_101.pdf for
`further explanation. See also d prime, detection error trade-off
`(DET), detect and identification rate, equal error rate, false
`acceptance rate (FAR), false alarm rate (FAR), false match rate,
`false non-match rate, false reject rate, identification rate,
`performance, verification rate.
`
`
`Algorithm
`A limited sequence of instructions or steps that tells a computer
`system how to solve a particular problem. A biometric system will
`have multiple algorithms, for example: image processing,
`template generation, comparisons, etc.
`
`
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`Biometrics Glossary
`
`ANSI - American National Standards Institute
`A private, non-profit organization that administers and
`coordinates the U.S. voluntary standardization and conformity
`assessment system. The mission of ANSI is to enhance both the
`global competitiveness of U.S. business and the U.S. quality of life
`by promoting and facilitating voluntary consensus standards and
`conformity assessment systems, and safeguarding their integrity.
`For more information visit www.ansi.org. See also INCITS, ISO,
`NIST.
`
`
`Application Programming Interface (API)
`Formatting instructions or tools used by an application developer
`to link and build hardware or software applications.
`
`
`Arch
`A fingerprint pattern in which the
`friction ridges enter from one side, make
`a rise in the center, and exit on the
`opposite side. The pattern will contain
`no true delta point. See also delta point,
`loop, whorl.
`
`
`Attempt
`The submission of a single set of biometric sample to a biometric
`system for identification or verification. Some biometric systems
`permit more than one attempt to identify or verify an individual.
`See also biometric sample, identification, verification.
`
`
`Authentication
`1. The process of establishing confidence in the truth of some
`claim. The claim could be any declarative statement for
`example: “This individual’s name is ‘Joseph K.’ ” or “This child
`is more than 5 feet tall.”
`2. In biometrics, “authentication” is sometimes used as a generic
`synonym for verification. See also verification.
`
`
`
`
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`
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`Biometrics Glossary
`
`Automated Biometric Identification System (ABIS)
`1. Department of Defense (DOD) system implemented to improve
`the U.S. government's ability to track and identify national
`security threats. The system includes mandatory collection of
`ten rolled fingerprints, a minimum of five mug shots from
`varying angles, and an oral swab to collect DNA.
`2. Generic term sometimes used in the biometrics community to
`discuss a biometric system. See also AFIS.
`
`
`
`Automated Fingerprint Identification System (AFIS)
`A highly specialized biometric system that compares a submitted
`fingerprint record (usually of multiple fingers) to a database of
`records, to determine the identity of an individual. AFIS is
`predominantly used for law enforcement, but is also being used
`for civil applications (e.g. background checks for soccer coaches,
`etc). See also IAFIS.
`
`
`Behavioral Biometric Characteristic
`A biometric characteristic that is learned and acquired over time
`rather than one based primarily on biology. All biometric
`characteristics depend somewhat upon both behavioral and
`biological characteristic. Examples of biometric modalities for
`which behavioral characteristics may dominate include signature
`recognition and keystroke dynamics. See also biological biometric
`characteristic.
`
`
`Benchmarking
`The process of comparing measured performance against a
`standard, openly available, reference.
`
`
`Bifurcation
`The point in a fingerprint where a friction
`ridge divides or splits to form two ridges, as
`illustrated below. See also friction ridge,
`minutia(e) point, ridge ending.
`
`
`
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`
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`Biometrics Glossary
`
`Binning
`Process of parsing (examining) or classifying data in order to
`accelerate and/or improve biometric matching.
`
`
`BioAPI – Biometrics Application Programming Interface
`Defines the application programming interface and service
`provider interface for a standard biometric technology interface.
`The BioAPI enables biometric devices to be easily installed,
`integrated or swapped within the overall system architecture.
`
`
`Biological Biometric Characteristic
`A biometric characteristic based primarily on an anatomical or
`physiological characteristic, rather than a learned behavior. All
`biometric characteristics depend somewhat upon both behavioral
`and biological characteristic. Examples of biometric modalities
`for which biological characteristics may dominate include
`fingerprint and hand geometry. See also behavioral biometric
`characteristic.
`
`
`Biometrics
`A general term used alternatively to describe a characteristic or a
`process.
`As a characteristic:
`A measurable biological (anatomical and physiological) and
`behavioral characteristic that can be used for automated
`recognition.
`As a process:
`Automated methods of recognizing an individual based on
`measurable biological (anatomical and physiological) and
`behavioral characteristics.
`
`
`
`Biometric Consortium (BC)
`An open forum to share information throughout government,
`industry, and academia. For more information visit
`www.biometrics.org.
`
`
`
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`
`
`Biometrics Glossary
`
`Biometric Data
`A catch-all phrase for computer data created during a biometric
`process. It encompasses raw sensor observations, biometric
`samples, models, templates and/or similarity scores. Biometric
`data is used to describe the information collected during an
`enrollment, verification, or identification process, but does not
`apply to end user information such as user name, demographic
`information and authorizations.
`
`
`Biometric Sample
`Information or computer data obtained from a biometric sensor
`device. Examples are images of a face or fingerprint.
`
`
`Biometric System
`Multiple individual components (such as sensor, matching
`algorithm, and result display) that combine to make a fully
`operational system. A biometric system is an automated system
`capable of:
`1. Capturing a biometric sample from an end user
`2. Extracting and processing the biometric data from that
`sample
`3. Storing the extracted information in a database
`4. Comparing the biometric data with data contained in one
`or more reference references
`5. Deciding how well they match and indicating whether or
`not an identification or verification of identity has been
`achieved.
`A biometric system may be a component of a larger system.
`
`
`Capture
`The process of collecting a biometric sample from an individual
`via a sensor. See also submission.
`
`
`
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`
`
`Biometrics Glossary
`
`CBEFF - Common Biometric Exchange Formats Framework
`A standard that provides the ability for a system to identify, and
`interface with, multiple biometric systems, and to exchange data
`between system components.
`
`
`Challenge Response
`A method used to confirm the presence of a person by eliciting
`direct responses from the individual. Responses can be either
`voluntary or involuntary. In a voluntary response, the end user will
`consciously react to something that the system presents. In an
`involuntary response, the end user's body automatically responds
`to a stimulus. A challenge response can be used to protect the
`system against attacks. See also liveness detection.
`
`
`Claim of identity
`A statement that a person is or is not the source of a reference in
`a database. Claims can be positive (I am in the database),
`negative (I am not in the database) or specific (I am end user 123
`in the database).
`
`
`Closed-set Identification
`A biometric task where an unidentified individual is known to be
`in the database and the system attempts to determine his/her
`identity. Performance is measured by the frequency with which
`the individual appears in the system’s top rank (or top 5, 10,
`etc.). See also identification, open-set identification.
`
`
`Comparison
`Process of comparing a biometric reference with a previously
`stored reference or references in order to make an identification
`or verification decision. See also match.
`
`
`Cooperative User
`An individual that willingly provides his/her biometric to the
`biometric system for capture. Example: A worker submits his/her
`
`
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`
`
`Biometrics Glossary
`
`biometric to clock in and out of work. See also indifferent user,
`non-cooperative user, uncooperative user.
`
`
`Core Point
`The "center(s)" of a fingerprint. In a whorl pattern, the core point
`is found in the middle of the spiral/circles. In a loop pattern, the
`core point is found in the top region of the innermost loop. More
`technically, a core point is defined as the topmost point on the
`innermost upwardly curving friction ridgeline. A fingerprint may
`have multiple cores or no cores. See also arch, delta point,
`friction ridge, loop, whorl.
`
`
`
`
`
`
`Covert
`An instance in which biometric samples are being collected at a
`location that is not known to bystanders. An example of a covert
`environment might involve an airport checkpoint where face
`images of passengers are captured and compared to a watchlist
`without their knowledge. See also non-cooperative user, overt.
`
`
`Crossover Error Rate (CER)
`See equal error rate (EER).
`
`
`Cumulative Match Characteristic (CMC)
`A method of showing measured accuracy performance of a
`biometric system operating in the closed-set identification task.
`
`
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`
`
`Biometrics Glossary
`
`Templates are compared and ranked based on their similarity.
`The CMC shows how often the individual’s template appears in the
`ranks (1, 5, 10, 100, etc.), based on the match rate. A CMC
`compares the rank (1, 5, 10, 100, etc.) versus identification rate
`as illustrated below.
`
`
`
`
`Cumulative Match CharacteristicCumulative Match Characteristic
`
`
`1.001.00
`1.00
`
`0.950.95
`0.95
`
`0.900.90
`0.90
`
`0.850.85
`0.85
`
`0.800.80
`0.80
`
`0.750.75
`0.75
`
`0.700.70
`0.70
`
`0.650.65
`0.65
`
`0.600.60
`0.60
`
`0.550.55
`0.55
`
`0.500.50
`0.50
`
`Probability of Identification
`Probability of Identification
`
`
`
`00
`
`
`
`2020
`
`
`
`4040
`
`
`
`6060
`
`
`
`8080
`
`
`
`100100
`
`
`
`120120
`
`
`
`140140
`
`
`
`RankRank
`
`
`
`
`
`D-Prime (D’)
`A statistical measure of how well a system can discriminate
`between a signal and a non-signal.
`
`
`Database
`A collection of one or more computer files. For biometric
`systems, these files could consist of biometric sensor readings,
`templates, match results, related end user information, etc. See
`also gallery.
`
`
`Decision
`The resultant action taken (either automated or manual) based on
`a comparison of a similarity score (or similar measure) and the
`system’s threshold. See also comparison, similarity score,
`threshold.
`
`
`
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`
`
`Biometrics Glossary
`
`Degrees of Freedom
`A statistical measure of how unique biometric data is.
`Technically, it is the number of statistically independent features
`(parameters) contained in biometric data.
`
`
`Delta Point
`Part of a fingerprint pattern that looks
`similar to the Greek letter delta ((cid:31)), as
`illustrated below. Technically, it is the
`point on a friction ridge at or nearest to
`the point of divergence of two type lines,
`and located at or directly in front of the
`point of divergence. See also core point,
`friction ridge.
`
`
`Detection and Identification Rate
`The rate at which individuals, who are in a database, are properly
`identified in an open-set identification (watchlist) application.
`See also open-set identification, watchlist.
`
`
`Detection Error Trade-off (DET) Curve
`A graphical plot of measured error rates, as illustrated below.
`DET curves typically plot matching error rates (false non-match
`rate vs. false match rate) or decision error rates (false reject rate
`vs. false accept rate). See also Receiver Operating
`Characteristics.
`
`
`
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`
`
`Biometrics Glossary
`
`Difference Score
`A value returned by a biometric algorithm that indicates the
`degree of difference between a biometric sample and a
`reference. See also hamming distance, similarity score.
`
`
`Eavesdropping
`Surreptitiously obtaining data from an unknowing end user who is
`performing a legitimate function. An example involves having a
`hidden sensor co-located with the legitimate sensor. See also
`skimming.
`
`
`EFTS - Electronic Fingerprint Transmission Specification
`A document that specifies requirements to which agencies must
`adhere to communicate electronically with the Federal Bureau of
`Investigation’s Integrated Automated Fingerprint Identification
`System (IAFIS). This specification facilitates information sharing
`and eliminates the delays associated with fingerprint cards. See
`also Integrated Automated Fingerprint Identification System
`(IAFIS).
`
`
`Encryption
`The act of transforming data into an unintelligible form so that it
`cannot be read by unauthorized individuals. A key or a password
`is used to decrypt (decode) the encrypted data.
`
`
`End User
`The individual who will interact with the system to enroll, to
`verify, or to identify. See also cooperative user, indifferent user,
`non-cooperative user, uncooperative user, user.
`
`
`Enrollment
`The process of collecting a biometric sample from an end user,
`converting it into a biometric reference, and storing it in the
`biometric system’s database for later comparison.
`
`
`
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`
`
`Biometrics Glossary
`
`Equal Error Rate (EER)
`A statistic used to show biometric performance, typically when
`operating in the verification task. The EER is the location on a
`ROC or DET curve where the false accept rate and false reject
`rate (or one minus the verification rate {1-VR}) are equal, as
`illustrated below. In general, the lower the equal error rate
`value, the higher the accuracy of the biometric system. Note,
`however, that most operational systems are not set to operate at
`the “equal error rate” so the measure’s true usefulness is limited
`to comparing biometric system performance. The EER is
`sometimes referred to as the “Crossover Error Rate.” See also
`Detection Error Trade-off (DET) curve, false accept rate, false
`reject rate, Receiver Operating Characteristics (ROC).
`
`
`
`
`
`
`Extraction
`The process of converting a captured biometric sample into
`biometric data so that it can be compared to a reference. See
`also biometric sample, feature, template.
`
`
`Face Recognition
`A biometric modality that uses an image of the visible physical
`structure of an individual’s face for recognition purposes.
`
`
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`
`
`Biometrics Glossary
`
`Failure to Acquire (FTA)
` Failure of a biometric system to capture and/or extract usable
`information from a biometric sample.
`
`
`Failure to Enroll (FTE)
`Failure of a biometric system to form a proper enrollment
`reference for an end user. Common failures include end users
`who are not properly trained to provide their biometrics, the
`sensor not capturing information correctly, or captured sensor
`data of insufficient quality to develop a template.
`
`
`False Acceptance Rate (FAR)
`A statistic used to measure biometric performance when
`operating in the verification task. The percentage of times a
`system produces a false accept, which occurs when an individual
`is incorrectly matched to another individual’s existing biometric.
`Example: Frank claims to be John and the system verifies the
`claim. See also false match rate, type II error.
`
`
`False Alarm Rate
`A statistic used to measure biometric performance when
`operating in the open-set identification (sometimes referred to as
`watchlist) task. This is the percentage of times an alarm is
`incorrectly sounded on an individual who is not in the biometric
`system’s database (the system alarms on Frank when Frank isn’t
`in the database), or an alarm is sounded but the wrong person is
`identified (the system alarms on John when John is in the
`database, but the system thinks John is Steve).
`
`
`False Match Rate
`A statistic used to measure biometric performance when. Similar
`to the False Acceptance Rate (FAR).
`
`
`
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`
`
`Biometrics Glossary
`
`False Non-Match Rate
`A statistic used to measure biometric performance. Similar to the
`False Reject Rate (FRR), except the FRR includes the Failure To
`Acquire error rate and the False Non-Match Rate does not.
`
`
`False Rejection Rate (FRR)
`A statistic used to measure biometric performance when
`operating in the verification task. The percentage of times the
`system produces a false reject. A false reject occurs when an
`individual is not matched to his/her own existing biometric
`template. Example: John claims to be John, but the system
`incorrectly denies the claim. See also false non-match rate, type
`I error.
`
`
`Feature(s)
`Distinctive mathematical characteristic(s) derived from a
`biometric sample; used to generate a reference. See also
`extraction, template.
`
`
`Feature Extraction
`See extraction.
`
`
`FERET - FacE REcognition Technology program
`A face recognition development and evaluation program
`sponsored by the U.S. Government from 1993 through 1997. For
`more information visit www.frvt.org/FERET/default.htm. See also
`FRGC, FRVT.
`
`
`Fingerprint Recognition
`A biometric modality that uses the physical structure of an
`individual’s fingerprint for recognition purposes. Important
`features used in most fingerprint recognition systems are minutiae
`points that include bifurcations and ridge endings. See also
`bifurcation, core point, delta point, minutia(e) point.
`
`
`
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`
`
`Biometrics Glossary
`
`FpVTE - Fingerprint Vendor Technology Evaluation (2003)
`An independently administered technology evaluation of
`commercial fingerprint matching algorithms. For more
`information visit fpvte.nist.gov.
`
`
`FRGC - Face Recognition Grand Challenge
`A face recognition development program sponsored by the U.S.
`Government from 2003-2005. For more information visit
`www.frvt.org/FRGC. See also FERET, FRVT.
`
`
`Friction Ridge
`The ridges present on the skin of the fingers and toes, and on the
`palms and soles of the feet, which make contact with an incident
`surface under normal touch. On the fingers, the distinctive
`patterns formed by the friction ridges that make up the
`fingerprints. See also minutia(e) point.
`
`
`FRVT - Face Recognition Vendor Test
`A series of large-scale independent technology evaluations of face
`recognition systems. The evaluations have occurred in 2000, 2002,
`and 2005. For more information visit
`www.frvt.org/FRVT2005/default.aspx. See also FRGC, FERET.
`
`
`Gallery
`The biometric system’s database, or set of known individuals, for
`a specific implementation or evaluation experiment. See also
`database, probe.
`
`
`Gait
`An individual’s manner of walking. This behavioral characteristic
`is in the research and development stage of automation.
`
`
`
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`Biometrics Glossary
`
`Hamming Distance
`The number of non-corresponding digits in a string of binary
`digits; used to measure dissimilarity. Hamming distances are used
`in many Daugman iris recognition algorithms. See also difference
`score, similarity score.
`
`
`Hand Geometry Recognition
`A biometric modality that uses the physical structure of an
`individual’s hand for recognition purposes.
`
`
`ICE - Iris Challenge Evaluation
`A large-scale development and independent technology evaluation
`activity for iris recognition systems sponsored by the U.S.
`Government in 2005. .For more information visit iris.nist.gov/ICE.
`
`
`Identification
`A task where the biometric system searches a database for a
`reference matching a submitted biometric sample, and if found,
`returns a corresponding identity. A biometric is collected and
`compared to all the references in a database. Identification is
`“closed-set” if the person is known to exist in the database. In
`“open-set” identification, sometimes referred to as a “watchlist,”
`the person is not guaranteed to exist in the database. The system
`must determine whether the person is in the database, then
`return the identity. See also closed-set identification, open-set
`identification, verification, watchlist.
`
`
`Identification Rate
`The rate at which an individual in a database is correctly
`identified.
`
`
`Identity Governance
`The combination of policies and actions taken to ensure
`enterprise-wide consistency, privacy protection and appropriate
`interoperability between individual identity management systems.
`
`
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`
`
`Biometrics Glossary
`
`Identity Management
`The combination of systems, rules and procedures that defines an
`agreement between an individual and organization(s) regarding
`ownership, utilization and safeguard of personal identity
`information.
`
`
`Impostor
`A person who submits a biometric sample in either an intentional
`or inadvertent attempt to claim the identity of another person to
`a biometric system. See also attempt.
`
`
`INCITS - International Committee for Information Technology
`Standards
`Organization that promotes the effective use of information and
`communication technology through standardization in a way that
`balances the interests of all stakeholders and increases the global
`competitiveness of the member organizations. For more
`information visit www.INCITS.org. See also ANSI, ISO, NIST.
`
`
`Indifferent User
`An individual who knows his/her biometric sample is being
`collected and does not attempt to help or hinder the collection of
`the sample. For example, an individual, aware that a camera is
`being used for face recognition, looks in the general direction of
`the sensor, neither avoiding nor directly looking at it. See also
`cooperative user, non-cooperative user, uncooperative user.
`
`
`Infrared
`Light that lies outside the human visible spectrum at its red (low
`frequency) end.
`
`
`Integrated Automated Fingerprint Identification System (IAFIS)
`The FBI’s large-scale ten fingerprint (open-set) identification
`system that is used for criminal history background checks and
`identification of latent prints discovered at crime scenes. This
`system provides automated and latent search capabilities,
`
`
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`
`
`Biometrics Glossary
`
`electronic image storage, and electronic exchange of fingerprints
`and responses. See also AFIS.
`
`
`Iris Recognition
`A biometric modality that uses an image of the
`physical structure of an individual’s iris for
`recognition purposes, as illustrated below. The
`iris muscle is the colored portion of the eye
`surrounding the pupil.
`
`
`IrisCode©
`A biometric feature format used in the Daugman iris recognition
`system.
`
`
`ISO - International Organization for Standardization
`A non-governmental network of the national standards institutes
`from 151 countries. The ISO acts as a bridging organization in
`which a consensus can be reached on solutions that meet both the
`requirements of business and the broader needs of society, such
`as the needs of stakeholder groups like consumers and users. For
`more information visit www.iso.org. See also ANSI, INCITS, NIST.
`
`
`Keystroke Dynamics
`A biometric modality that uses the cadence of an individual’s
`typing pattern for recognition.
`
`
`Latent Fingerprint
`A fingerprint “image” left on a surface that was touched by an
`individual. The transferred impression is left by the surface
`contact with the friction ridges, usually caused by the oily
`residues produced by the sweat glands in the finger. See also
`friction ridge.
`
`
`
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`
`
`Biometrics Glossary
`
`Live Capture
`Typically refers to a fingerprint capture device that electronically
`captures fingerprint images using a sensor (rather than scanning
`ink-based fingerprint images on a card or lifting a latent
`fingerprint from a surface). See also sensor.
`
`
`Liveness Detection
`A technique used to ensure that the biometric sample submitted
`is from an end user. A liveness detection method can help protect
`the system against some types of spoofing attacks. See also
`challenge response, mimic, spoofing.
`
`
`Loop
`A fingerprint pattern in which the friction
`ridges enter from either side, curve sharply
`and pass out near the same side they entered
`as illustrated below. This pattern will contain
`one core and one delta. See also arch, core
`point, delta point, friction ridge, whorl.
`
`
`Match
`A decision that a biometric sample and a stored template comes
`from the same human source, based on their high level of
`similarity (difference or hamming distance). See also false match
`rate, false non-match rate.
`
`
`Matching
`The process of comparing a biometric sample against a previously
`stored template and scoring the level of similarity (difference or
`hamming distance). Systems then make decisions based on this
`score and its relationship (above or below) a predetermined
`threshold. See also comparison, difference score, threshold.
`
`
`
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`
`
`Biometrics Glossary
`
`Mimic
`The presentation of a live biometric measure in an attempt to
`fraudulently impersonate someone other than the submitter. See
`also challenge response, liveness detection, spoofing.
`
`
`Minutia(e) Point
`Friction ridge characteristics that are used to individualize a
`fingerprint image, see illustration below. Minutiae are the points
`where friction ridges begin, terminate, or split into two or more
`ridges. In many fingerprint systems, the minutiae (as opposed to
`the images) are compared for recognition purposes. See also
`friction ridge, ridge ending.
`
`
`
`
`
`
`
`Modality
`A type or class of biometric system. For example: face
`recognition, fingerprint recognition, iris recognition, etc.
`
`
`Model
`A representation used to characterize an individual. Behavioral-
`based biometric systems, because of the inherently dynamic
`characteristics, use models rather than static templates. See also
`template.
`
`
`Multimodal Biometric System
`A biometric system in which two or more of the modality
`components (biometric characteristic, sensor type or feature
`extraction algorithm) occurs in multiple.
`
`
`Neural Net/Neural Network
`A type of algorithm that learns from past experience to make
`decisions. See also algorithm.
`
`
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`Biometrics Glossary
`
`NIST - National Institute of Standards and Technology
`A non-regulatory federal agency within the U.S. Department of
`Commerce that develops and promotes measurement, standards,
`and technology to enhance productivity, facilitate trade, and
`improve the quality of life. NIST’s measurement and standards
`work promotes the well-being of the nation and helps improve,
`among many others things, the nation’s homeland security. For
`more information visit www.nist.gov. See also ANSI, INCITS, ISO.
`
`
`Noise
`Unwanted components in a signal that degrade the quality of data
`or interfere with the desired signals processed by a system.
`
`
`Non-cooperative User
`An individual who is not aware that his/her biometric sample is
`being collected. Example: A traveler passing through a security
`line at an airport is unaware that a camera is capturing his/her
`face image. See also cooperative user, indifferent user,
`uncooperative user.
`
`
`One-to-many
`A phrase used in the biometrics community to describe a system
`that compares one reference to many enrolled references to
`make a decision. The phrase typically refers to the identification
`or watchlist tasks.
`
`
`One-to-one
`A phrase used in the biometrics community to describe a system
`that compares one reference to one enrolled reference to make a
`decision. The phrase typically refers to the verification task
`(though not all verification tasks are truly one-to-one) and the
`identification task can be accomplished by a series of one-to-one
`comparisons.
`
`
`
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`Biometrics Glossary
`
`Open-set Identification
`Biometric task that more closely follows operational biometric
`system conditions to 1) determine if someone is in a database and
`2) find the record of the individual in the database. This is
`sometimes referred to as the “watchlist” task to differentiate it
`from the more commonly referenced closed-set identification.
`See also closed-set identification, identification.
`
`
`Operational Evaluation
`One of the three types of performance evaluations. The primary
`goal of an operational evaluation is to determine the workflow
`impact seen by the addition of a biometric system. See also
`technology evaluation, scenario evaluation.
`
`
`Overt
`Biometric sample collection where end users know they are being
`collected and at what location. An example of an overt
`environment is the US-VISIT program where non-U.S. citizens
`entering the United States submit their fingerprint data. See also
`covert.
`
`
`Palm Print Recognition
`A biometric modality that uses the physical
`structure of an individual’s palm print for
`recognition purposes, as illustrated below.
`
`
`Performance
`A catch-all phrase for describing a measurement of the
`characteristics, such as accuracy or speed, of a biometric
`algorithm or system. See also accuracy, crossover error rate,
`cumulative match characteristics, d-prime, detection error trade-
`off, equal error rate, false accept rate, false alarm rate, false
`match rate, false reject rate, identification rate, operational
`evaluation, receiver operating characteristics, scenario
`evaluation, technology evaluation, true accept rate, true reject
`rate, verification rate.
`
`
`
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`
`Biometrics Glossary
`
`PIN - Personal Identification Number
`A security method used to show “what you know.” Depending on
`the system, a PIN could be used to either claim or verify a
`claimed identity.
`
`
`Pixel
`A picture element. This is the smallest element of a display that
`can be assigned a color value. See also pixels per inch (PPI),
`resolution.
`
`
`Pixels Per Inch (PPI)
`A measure of the resolution of a digital image. The higher the
`PPI, the more information is included in the image, and the larger
`the file size. See also pixel, resolution.
`
`
`Population
`The set of potential end users for an application.
`
`
`Probe
`The biometric sample that is submitted to the biometric system to
`compare against one or more references in the gallery. See also
`gallery.
`
`
`Radio Frequency Identification (RFID)
`Technology that uses low-powered radio transmitters to read data
`stored in a transponder (tag). RFID tags can be used to track
`assets, manage inventory, authorize payments, and serve as
`electronic keys. RFID is not a biometric.
`
`
`Receiver Operating Characteristics (ROC)
`A method of showing measured accuracy performance of a
`biometric system. A verification ROC compares false