Biometrics

Biometrics
This content has subsequently be published as a white paper on the "European Biometrics Portal"
Fusion Comes in from the Cold
Biometrics is the science and technology of measuring and statistically analyzing biological data. In information technology, biometrics usually refers to automated technologies for measuring and analyzing an individual’s physical and behavioural characteristics; such as fingerprints, irises, voice patterns, facial patterns and gait. The analysis of such data is then used for identification or verification purposes, depending on need. Multi-modal biometrics, or biometric fusion, is the process of combining information from multiple biometric readings, either before, during or after a decision has been made regarding identification or authentication from a single biometric.
Multi-modal Biometrics
Before considering multi-modal biometrics it is important to understand the core features of a conventional (uni-modal) biometric system. Such a system can be decomposed into four components:
Biometric Capture Module, e.g. a fingerprint reader or iris scanner;
Feature Extraction Module, software to select the active matching data1 and produce a feature vector2 of the biometric;
Matching Module, that compares the captured biometric with an existing database;
Decision module, that provides a degree of confidence in any identity matched against the person providing the biometric sample.
1. Features extracted from the biometric reading that will be used to create the feature vector;
2. The measurements extracted from the active matching data describe the useful image features and thus are known as a feature vector.
Figure 1:
The core components of a single biometric system.
The ability of the system to perform well (within the limits of its design) is based almost solely upon the quality of the biometric captured. A well captured biometric is rich in distinguishing information, which in turn gives the feature extraction algorithms the best chance of finding a match with existing records.
However, the ability of the system to capture high quality biometric samples is reduced by many factors. Dirty fingerprint sensors, photographic light intensity or a voice altered by a cold, may all reduce quality of a reading to the extent that multiple readings of the same biometric (e.g., the same person’s index finger) can produce a range of widely-differing samples. Add to this the fact that a minority of individuals may not be able to provide a given biometrics (e.g., the fingerprints of manual workers are often too degraded to be captured well), and a conventional biometric system may soon become a burden on its operators.
The accuracy of a biometric system can be measured from two statistical measurements:
1. The False Match Rate (FMR), also known as a Type I error or False Acceptance Rate (FAR) is a measure of the readings that the system incorrectly matches to its database. The lower the FAR, the better a system’s security.
2. The False non-Match rate (FnMR), also known as a Type II error or False Rejection Rate (FRR) is a measure of the readings that the system incorrectly fails to match to its database. The lower the FRR, the easier a system will be to use.
The FAR and the FRR are inversely proportional, forcing a trade-off between security and convenience when using biometric systems. A system that is easy to use, meaning convenient for both user and system administrator, may allow unauthorised access to a secure area, whilst a highly secure system may require continual human intervention to allow access to authorised users not recognised by the device. Multi-modal biometric systems may be able to improve this trade off.
Multi-Modal Biometric Systems
Multi-modal biometric systems capture two or more biometric samples and use fusion to combine their analyses to produce a (hopefully) better match decision by simultaneously decreasing the FAR and FRR.
Fusion Methodology
Multi-modal biometrics systems can be designed to work in five ways:<
Multiple sensors may be used to capture the same biometric;
Multiple biometrics may be captured;
Multiple readings of the same biometric may be combined to achieve an optimal reading;
Readings of two or more units of the same biometric may be taken (e.g., two different fingerprints or both irises) or;
Different matching and/or feature extraction algorithms may be used on the same biometric reading to give independent results.
A combination of uncorrelated modalities (e.g., fingerprint and face or two fingers) is usually expected to result in a better performance than a combination of correlated modalities [Jain], (e.g., multiple captures of the same finger or multiple matching algorithms). Biometrics
This content has subsequently be published as a white paper on the "European Biometrics Portal"
Fusion Comes in from the Cold
Biometrics is the science and technology of measuring and statistically analyzing biological data. In information technology, biometrics usually refers to automated technologies for measuring and analyzing an individual’s physical and behavioural characteristics; such as fingerprints, irises, voice patterns, facial patterns and gait. The analysis of such data is then used for identification or verification purposes, depending on need. Multi-modal biometrics, or biometric fusion, is the process of combining information from multiple biometric readings, either before, during or after a decision has been made regarding identification or authentication from a single biometric.
Multi-modal Biometrics
Before considering multi-modal biometrics it is important to understand the core features of a conventional (uni-modal) biometric system. Such a system can be decomposed into four components:
Biometric Capture Module, e.g. a fingerprint reader or iris scanner;
Feature Extraction Module, software to select the active matching data1 and produce a feature vector2 of the biometric;
Matching Module, that compares the captured biometric with an existing database;
Decision module, that provides a degree of confidence in any identity matched against the person providing the biometric sample.
1. Features extracted from the biometric reading that will be used to create the feature vector;
2. The measurements extracted from the active matching data describe the useful image features and thus are known as a feature vector.
Figure 1:
The core components of a single biometric system.
The ability of the system to perform well (within the limits of its design) is based almost solely upon the quality of the biometric captured. A well captured biometric is rich in distinguishing information, which in turn gives the feature extraction algorithms the best chance of finding a match with existing records.
However, the ability of the system to capture high quality biometric samples is reduced by many factors. Dirty fingerprint sensors, photographic light intensity or a voice altered by a cold, may all reduce quality of a reading to the extent that multiple readings of the same biometric (e.g., the same person’s index finger) can produce a range of widely-differing samples. Add to this the fact that a minority of individuals may not be able to provide a given biometrics (e.g., the fingerprints of manual workers are often too degraded to be captured well), and a conventional biometric system may soon become a burden on its operators.
The accuracy of a biometric system can be measured from two statistical measurements:
1. The False Match Rate (FMR), also known as a Type I error or False Acceptance Rate (FAR) is a measure of the readings that the system incorrectly matches to its database. The lower the FAR, the better a system’s security.
2. The False non-Match rate (FnMR), also known as a Type II error or False Rejection Rate (FRR) is a measure of the readings that the system incorrectly fails to match to its database. The lower the FRR, the easier a system will be to use.
The FAR and the FRR are inversely proportional, forcing a trade-off between security and convenience when using biometric systems. A system that is easy to use, meaning convenient for both user and system administrator, may allow unauthorised access to a secure area, whilst a highly secure system may require continual human intervention to allow access to authorised users not recognised by the device. Multi-modal biometric systems may be able to improve this trade off.
Multi-Modal Biometric Systems
Multi-modal biometric systems capture two or more biometric samples and use fusion to combine their analyses to produce a (hopefully) better match decision by simultaneously decreasing the FAR and FRR.
Fusion Methodology
Multi-modal biometrics systems can be designed to work in five ways:<
Multiple sensors may be used to capture the same biometric;
Multiple biometrics may be captured;
Multiple readings of the same biometric may be combined to achieve an optimal reading;
Readings of two or more units of the same biometric may be taken (e.g., two different fingerprints or both irises) or;
Different matching and/or feature extraction algorithms may be used on the same biometric reading to give independent results.
A combination of uncorrelated modalities (e.g., fingerprint and face or two fingers) is usually expected to result in a better performance than a combination of correlated modalities [Jain], (e.g., multiple captures of the same finger or multiple matching algorithms).