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Biometric Technology Terminology
The language of biometrics is an evolving lexicon of science and technology. Below are some commonly used terms and their meanings.
2D Facial Landmarking
An advanced biometric process that captures and analyzes face image data, combined with facial feature tracking, facial modeling and animation, and expression analysis. It increases the accuracy of biometric systems by an order of magnitude.
ABIS stands for Automated Biometric Identification System which is a large scale customizable software solution similar to AFIS, but with the added advantages of utilizing multiple biometric modalities including iris and facial recognition to provide fast, secure, and reliable results.
Active Impostor Acceptance
Active Impostor Acceptance is when an access control system incorrectly recognizes and accepts a biometric sample which has been altered, modified, or cloned.
A sequence of instructions that instructs a biometric system on how to solve a problem. It could have a finite number of steps in the instruction for computing whether the sample and the template are matched.
The moment a biometric sample is being submitted for verification. An “attempt” may happen more than once in cases where the sample is denied or rejected.
When biometric data being used is considered to be correct and valid. “Validation” is the preferred term for this.
Physical traits or patterns which are unique to every individual that are used to verify and authenticate a person’s identity who is enrolled into a system. Biometric patterns can be anything from fingerprints, iris scans, facial recognition and even voice recognition.
The implementation of any system that involves biometric data.
Behavioral Biometrics identify individuals based on human actions and behaviors (e.g. keystroke dynamics, signature analysis, and gesture biometrics).
Biometric De-Duplication is a process of identifying duplicate biometric templates from the database to ensure every biometric template is unique.
The portion of the biometric software system that processes the gathered data. It can start to operate from the data capture, extraction, and comparison down to the matching.
Biometric Enrollment is the process of collecting an individual’s unique physical or behavioral characteristics using biometric hardware which is converted into a mathematical file using a complex algorithm and saved into the biometric database for future identification/authentication purposes.
Biometric Identification Device
A Biometric Identification Device is a device which gathers, reads, and compares biometric data. “Biometric System” is the term more often used.
The file containing the mathematical representation of a person’s biometric trait After a person’s unique physical or behavioral characteristics are captured by a biometric device, it is converted into a mathematical file using a complex algorithm and saved into the biometric database for future identification/authentication purposes.
Biometric Sample Data
The data captured by a system collected from a person of interest or a user.
Biometric SSO (Single Sign On)
An independent software system which authenticates access with a single biometric attribute within the network.
An automated system which does the following:
- Collects or captures biometric data via a scanner.
- Extracts the data from the actual submitted sample.
- Compares the scanned data from those capture for reference.
- Matching the submitted sample with the templates.
- Determining or verifying whether the identity of the biometric data holder is authentic.
The process of collecting biometric data from the end user or enrollee. Most biometric data are “captured” by the use of an image scanner in cases of fingerprints, palm vein patterns, or a camera to collect facial and iris scans.
Certification is testing gathered biometric data against a system or software to verify its ability to perform. The application will then be tested according to set standards for certifications, which are then issued by testing organizations.
A person who submits his biometric sample for identity verification. Claimants may either have true or false identities.
A biometric sample of an enrolled user of the system.
Closed Set Identification
Closed Set Identification is the process by which users need to be enrolled into a biometric system and verified in order for access to be granted.
Comparing a biometric sample with previously gathered samples or against a template or templates for verification of the identity.
Biometric technology that can identify an individual without having to make physical contact with a hardware device (e.g. iris and facial recognition), which is beneficial to certain hygienic environments such as patient and provider identification in healthcare.
Statistical measure which grades the ability of a system to distinguish between biometric samples or individuals. The higher D prime number means that the system is more capable of distinguishing between samples.
Degrees of Freedom
The number of independent features in a biometric system.
The conversion of any biometric data into a cypher code which cannot be easily read. A password or biometrics itself may be used as the key to decrypt or decode the data.
A currently enrolled or about to be enrolled individual who has his/her biometric data submitted for identification or verification.
End User Adaptation
End User Adaptation is the way in which users of a biometric system are able to adjust accordingly to it after becoming familiar with the test.
The user who has their biometric template entered into the system.
Gathering and processing of biometric data with the intent of storing records into a database.
The time spent on collecting the biometric data and successfully processing it.
Equal Error Rate
The frequency in which the rate of false rejection is almost equal to the rate of false acceptance.
The moment a biometric sample is converted into data which is later compared to a biometric template.
A process in which facial features are analyzed and gathered as biometric data.
Failure to Acquire
The process in which a biometric system fails to capture, extract, and store the data.
Failure to Acquire Rate
The number of times that a Failure to Acquire occurs.
Failure to Capture (FTC)
Failure to Capture (FTC) occurs when the system failed detect a biometric input, even though the input is correct.
Failure to Enroll (FTE)
Failure to Enroll (FTE) happens when the biometric system fails to enroll a person’s biometric attribute due to technical or environmental or damage due to accidental reasons.
The biometric system accepts either a false identity or incorrectly identifies a wrong identity against a claimed one.
False Accept Rate (FAR)
A metric used to measure the accuracy of biometric systems to correctly or incorrectly identify individuals that represents the statistical chance of incorrect matches. This is synonymous with the terms False Positive Identification Rate (FPIR) or False Match Rate (FMR).
An instance of an incorrect yet positive match outcome between submitted data and the enrolled database.
False Rejection occurs when a newly acquired sample of an enrolled identity is rejected by the system or when it fails to verify a legitimate identity.
False Rejection Rate (FRR)
The probability that a biometric system will fail to identify a legitimate identity due to the system failing to match the biometric input with the template. This is synonymous with the terms False Negative Identification Rate (FNIR) or False Non-Match Rate (FNMR).
Identification or Identity
A biometric sample which is matched against templates and other biometric references.
A person who poses as a verified user by submitting his own biometric sample.
In House Test
A series of testing done in a closed facility or laboratory. It may or may not involve the use of external participants or subjects.
A method to identify someone by a single iris or both irises (the colored, ring-shaped region surrounding the pupil of the eye).
The process of gathering biometric sample from a live user using a biometric system
Match or Matching
The process of matching a template to a submitted biometric sample. It is then rejected or accepted based on the whether the score has met the threshold or not.
A system that uses more than one physiological or behavioral characteristic such as iris and facial imaging, fingerprint and finger vein for enrollment, verification, and identification of a person.
Multi-Factor Authentication/Two Factor Authentication
A biometric system where more than one biometric credential is required to identify or authenticate a person. It increases the accuracy of biometric systems by an order of magnitude.
Identifying users who are not enrolled in the system, the opposite of closed set identification.
Original Equipment Manufacturer or Module
An organization which assembles a biometric system from different parts or an independent module which can be integrated into a biometric system.
Passive Impostor Acceptance
When an impostor’s submitted sample is verified and accepted by the system.
A set of standards or criteria which is used to evaluate the performance of the system.
Personal Identification Number (PIN)
A preset number is entered into a secured system to gain access. Usually a four-digit value.
Physiological Biometrics use parts of the human body for individual identification (e.g. fingerprints, finger vein, facial or iris recognition).
Receiver Operating Curves
A graph showing how the false rejection and false acceptance rates varies with one another
The widely used term is identification, due to a preset standard or set of traits.
The amount of time in which a biometric system analyzes a sample and returns with a decision.
Segmented identification (one-to-few, or 1: Few) involves confirming or denying a person’s claimed identity using a biometric scan followed by verbal confirmation to a general identification question (e.g., What is your date of birth?) or the entry of some known general information (gender, race, eye color).
The maximum capability of stored data in a biometric system.
Template or Reference Template Data
A biometric measurement which is used to verify succeeding biometric data.
Third Party Test
A test done by an independent party in a controlled environment.
Threshold or Decision Threshold
The acceptance level of any given biometric system. It may be tightened or widened accordingly to make the system meet certain requirements. If the data falls above or below the threshold, it is rejected. If the sample falls within the acceptable range it is accepted.
The number of users a biometric system can successfully process within a given time
Type 1 Error
See “False Rejection.”
Type 2 Error
See “False Acceptance.”
The client of any biometric vendor. Essentially, they are the clients that purchase the technology but may or may not enroll themselves into the system. End users are those who enroll their biometric data into the system.
The process of comparing a biometric sample with the biometric data in the system whose identity is claimed.
Verification (1:1 Matching)
Biometric verification is an identity authentication process used to confirm a person’s identity by matching their uniquely identifiable biometric traits such as fingerprints, palm vein, iris, and face recognition with a specific stored biometric template.
Zero Effort Forgery
An impostor uses the actual biometric sample of an enrolled user.
Sources and Further Reading
While biometric technologies like LightSpeed are cutting-edge in many ways, there has been extensive analysis of their importance and applications going back well over a decade. Here are a few seminal papers and articles for further reading.
- Building an Enterprise Master Person Index, AHIMA MPI Task Force, Journal of AHIMA 75, no. 1 (Jan. 2004): 56A–D
- Fifth Annual Study on Medical Identity Theft, Ponemon, 2014.
- Should Every Patient Have a Unique ID Number for All Medical Records?, Wall Street Journal Healthcare Report, July 2012.
- Identification errors involving clinical laboratories: a College of American Pathologists Q-Probes study of patient and specimen identification errors at 120 institutions, Valenstein, P. N., Raab, S. S. & Walsh, M. K. Arch. Pathol. Lab. Med. 130, 1106–1113 (2006).
- Hospital National Patient Safety Goals 2016, The Joint Commission, 2016.
- Technology Influence on Data Integrity & Impact on Patient Safety, Privacy & Security. AHIMA Convention Proceedings, September 2008.