Glossary of Biometric Terms and Technique Classifications has developed a fully cross-referenced glossary of technical terms concerning biometric technology. We understand that the language of biometrics can be quite complex and confusing. Below are some commonly used terms and their meanings. This glossary is developed to provide an accurate understanding of biometric technology and will be constantly updated for your convenience.

Active Impostor Acceptance– when an access control system incorrectly recognizes and accepts a biometric sample which has been altered, modified, or cloned.

Algorithm– 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 to use in computing whether the sample and the template are matched.

Application Program Interface (API)– a set of protocols use to standardize an application by a developer. For example, an API may be added or interchanged by an application developer into any biometric system.

Application Developer– an application programmer or manufacturer that develops and applies any software

Artificial Neural Network– an artificial intelligence system which allows learning to take place in the system. it may use past experiences and compute whether a biometric sample is a match with a template

ASIC or Application Specified Integrated Circuit-a silicon chip for a biometric system which is specifically produced to enhance performance

Attempt– the moment a biometric sample is being submitted for verification. An “attempt” may happen more than once in cases where it is denied or rejected.

Authentication– biometric data is considered to be correct and valid. “Validation” is the preferred term.

Behavioral Biometric– pattern of biometrics that is established after a given amount of time. It is not necessarily a physiological trait.

Biometric– a physical trait or pattern which is unique to every individual. It often 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 or even voice recognition.

Biometric Application– the implementation of any system that involves biometric data.

Biometric data– a sample taken from individual which is unique to their own person. Common biometric data are: fingerprint, voice and iris scans, palm vein patterns and even facial patterns.

Biometric Engine– the portion of the biometric software system that processes the gathered data. It can start to operate from the data capture, extraction, comparison down to the matching.

Biometric Identification Device– gathers, reads an compares biometric data. Biometric System is the term more often use.
Biometric Sample Data- the data captured by a system collected from a person of interest or a user.

Biometric System– an automated system which:

1. Collects or captures biometric data via a scanner
2. Extracts the data from the actual submitted sample
3. Compares the scanned data from those capture for reference
4. Matching the submitted sample with the templates
5. Determining or verifying whether the identity of the biometric data holder is authentic.

Biometric Taxonomy– a method of classification using gathered biometric data. It can also be the classification of biometric data according to their use in a given system such as:

• Cooperative versus Non-cooperative User
• Overt vs. Covert Biometric System
• Habituated vs. Non-habituated user
• Supervised vs. unsupervised User
• Standard Environment vs. Non-standard Environment

Biometric Technology– A system or application which is designed to employ biometric data. It can also be classified further according to the type of biometrics being used in the system.

Capture– the process of collecting biometric data from the end user or enrollee. Most biometric ata are “capture” by use of an image scanner in cases of fingerprints, palm vein patterns or a camera to collect facial an iris scans.

Certification– testing gathered biometric data against a system or software to verify its ability to perform. The application will be then tested according to set standards for certification. Testing organizations are the ones that issue certifications.

Comparison– comparing a biometric sample with previously gathered samples or against a template or templates for verification of the identity

Claimed Identity– a biometric sample of an enrolled user of the system

Claimant– person who submits his biometric sample for identity verification. Claimants may either have true or false identities.

Closed Set Identification-users need to be enrolled into a biometric system and verified for access to be granted

CMOS or Complementary Metal Oxide Semiconductor-a kind of circuit (integrated) used by some biometric systems due to its low power consumption

D Prime– 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

Encryption-the conversion of any biometric data into a code which cannot be easily read. A password may be used to decrypt or decode the data

End User– an enrolled or about to enroll individual who has his biometric data submitted for verification

End User Adaptation-users of a biometric system are able to adjust accordingly to it after being familiar with the test

Enroll-the user who has their biometric template entered into the system

Enrolment-gathering and processing of biometric data with the intent of storing them into a database

Enrollment Time-time spent the moment biometric data is collected and successfully processed

Equal Error Rate– the rate in which the rate of false rejection is almost equal to the rate of false acceptance

Extraction– the moment a biometric sample is converted into data after which it compared to a biometric template.

Failure to Acquire– a biometric system fails to capture, extract and store the ata

Failure to Acquire rate-the number of times that a failure to acquire occurs

False Acceptance– the biometric system accepts either a false identity or incorrectly identifies a wrong identity against a claimed one

False Match Rate-the moment a match between enrollee and submitted data is done which in turn results to a rejection

False Rejection– occurs when an enrolled identity is rejected by the system or when it fails to verify a legitimate identity

False Rejection Rate– the probability that a biometric system will fail to identify a legitimate identity

The equation is:


• FRR is the false rejection rate
• NFR- number of false rejections
• NEIA number of enrollee identification attempts
• NEVA-number of enrollee verification attempts

Field Test– a sample trial done in the outside or Real world

Goats Biometric System– pattern of activity done by system end-users which varies beyond the specified range allowed. Consequently, it may be rejected by the system.

Hamming Distance-a measure of dissimilarity. It is actually the disagreeing bits between two binary vectors.

Identification or Identity– biometric sample which is matched against templates and other biometric references

Impostor– a person who poses as a verified user by submitting his own biometric sample

In House Test– series of testing done in a closed facility or laboratory. It may or may not involve the use of external participants or subjects.

Live Capture– the actual process of gathering biometric sample from a live user using a biometric system

Match or Matching– the process of matching a template versus a submitted biometric sample. It is then rejected or accepted based on the whether the score has met the threshold or not.

Open- Set Identification– identifying users who are not enrolled in the system. 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.

Personal Identification Number (PIN)-usually a four digit number is entered into a system to gain access

Performance criteria– a set of standards or criteria which is used to evaluate the performance of the system

Physiological or Physical Biometric– a physical characteristic used as biometric data. This includes: fingerprints, face recognition, ear shape, iris recognition, palm and retina scans.

Receiver Operating curves– a graph showing how the false rejection and false acceptance rates varies with one another

Recognition– widely use term is identification

Response Time– the amount of time in which a biometric system analyzes a sample and returns with a decision

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– 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.

Throughput Rate– 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”

User– 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.

Validation-the process of comparing a biometric sample with the biometric data in the system whose identity is claimed

Wavelet Transform/Scalar quantization or WSQ-a compression algorithm used to compress used to reduce the size of reference templates

Zero Effort Forgery-an impostor uses the actual biometric sample of an enrolled user

Biometric Technology terms and technique classifications:

1. AFIS or Automated Fingerprint– a database of fingerprints used by law enforcement agencies. However, some civil or government agencies may also use the same database to verify identities.

Binning is one method of classification being used in some AFIS systems. Physical characteristics of the fingerprints such as loops, arches and whorls are further classified and stored in “Bins” according to their category. This method is used to make searches faster with a high egree of reliability.

2. Body Odor-a smell given off by the human body which is biometrically analyzed.

3. DNA– a human gene chain which is unique to every individual. Due to many underlying issues, the technology is not yet automatic and does not rank well alongside other biometric technologies

4. Ear Shape-biometrics of the ears

5. Face Recognition– facial features are analyzed and gathered as biometric data

Eigen Face– a method that represents the human face as a linear deviation from an average or mean face

Eigen Head– a 3d version of the Eigen Face

Face Monitoring– used for checking the attendance of a user t a desktop, it applies facial recognition technology.

Facial thermogram– detects and scans the heat signature from the face

6. Finger Image– takes the a scan of the patterns found at the tip of the finger.

Auto Correlation– two identical finger patterns are overlaid to create a Moire fringe.

Bifurcation-a branch mae by more than one finger image ridge.

Capacitance-a finger image capture technique that detects an electrical charge.

7. Finger Geometry– analyzes the shape of one or more fingers

8. Hand geometry or Hand recognition– analyzes and measures the shape of the hand

9. Iris Recognition– a biometric system that reads and scans iris features which is the colored ring the surrounds the pupil

10. Keystroke Dynamics– the typing rhythm of the end user is analyzed and gathered as behavioral biometric data

11. Palm– a biometric analysis of the palm of the hand

12. Retina– a biometric analysis of the blood vessels at the back of the eye

13. Signature verification– a behavioral biometric that analyses a signature made by the end user. Another signature verification may also analyze the speed, velocity and pressure exerted by the end user when signing his name and not just the image made.

14. Speaker Verification– a speech pattern analysis of a behavioral biometric.

Subsystems in this category are:

• Fixed text system
• Free text system
Speaker Dependent-is able to distinguish between voices
Speaker Separation– a system which is able to differentiate between voices and blot out background noise
Speech Recognition-recognizes the words but not the speaker
Speaker Verification Application Program Interface-API for speaker verification systems
Text Dependent System-requires a speaker to say a specific set of words
Text Independent System-creates voiceprints from unrestricted speech and does need a specific set of words to be spoken

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