Q. What is biometrics?

(1) General: Biometrics is the science of measuring physical properties of living beings. (2) ISO/IEC: Biometrics is the automated recognition of individuals based on their behavioral and biological characteristics.

Q. What is biometric recognition?

By measuring an individual's suitable behavioral and biological characteristics in a recognition inquiry and comparing these data with the biometric reference data which had been stored during a learning procedure, the identity of a specific user is determined.

Q. What is a biometric characteristic?

A biometric characteristic is biological or behavioural property of an individual that can be measured and from which distinguishing, repeatable biometric features can be extracted for the purpose of automated recognition of individuals. Example: face.

Q. What is a biometric sample?

A biometric sample is the analog or digital representation of biometric characteristics prior to the biometric feature extraction process and obtained from a biometric capture device or a biometric capture subsystem. Example: electronic face photograph. A biometric sample usually is delivered from a sensor, the main component of a biometric capture device. Generally, the biometric sample, often called raw data, comprises more information than is necessary for recognition. In many cases, the biometric sample is a direct image of the biometric characteristic such as a photograph.

Q. What are biometric features?

Biometric features are information extracted from biometric samples which can be used for comparison with a biometric reference. Example: characteristic measures extracted from a face photograph such as eye distance or nose size etc. The aim of the extraction of biometric features from a biometric sample is to remove any superfluous information which does not contribute to biometric recognition. This enables a fast comparison, an improved biometric performance, and may have privacy advantages.

Q. What is a biometric reference?

A biometric reference comprises one or more stored biometric samples, biometric templates, or biometric models attributed to a biometric data subject which can be used for comparison. Stored biometric features are called a biometric template. A biometric model is a stored function (dependent on the biometric data subject) generated from biometric features which is applied to the biometric features of a recognition biometric sample during a comparison to give a comparison result.

Q. What is a biometric template?

A biometric template is a special case of a biometric reference, where biometric features have been stored for the purpose of a comparison. (The comparison is done during the recognition process between the stored biometric template and the actual biometric features which have been extracted from the biometric data coming from the biometric capture device resp. sensor.)

Q. What is enrollment?

To be able to recognize a person by their biometric characteristics and the derived biometric features, first a learning phase must take place. The procedure is called enrolment and comprehends the creation of an enrolment data record of the biometric data subject (the person to be enrolled) and to store it in a biometric enrolment database. The enrolment data record comprises one or multiple biometric references and arbitrary non-biometric data such as a name or a personnel number.

Q. How does biometric recognition work?

For the purpose of recognition, the biometric data subject (the person to be recognized) presents his or her biometric characteristic to the biometric capture device which generates a recognition biometric sample from it. From the recognition biometric sample the biometric feature extraction creates biometric features which are compared with one or multiple biometric templates from the biometric enrolment database. Due to the statistical nature of biometric samples there is generally no exact match possible. For that reason, the decision process will only assign the biometric data subject to a biometric template and confirm recognition if the comparison score exceeds an adjustable threshold.

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