Current “Authentication” Technology is not tied to individual and it tied to the device or the tool that link to the device only.
Biometric is the technology which identified the individual based on their physical or behavior.
Physical consist of :
- Optical Recognition (Iris or Retina)
- Face Recognition
Behavior consist of :
Human characteristic can be used for biometrics in terms of the following parameters:
- Universality – each person should have the characteristic.
- Uniqueness – is how well the biometric separates individuals from another.
- Permanence – measures how well a biometric resists aging.
- Collectability – ease of acquisition for measurement.
- Performance – accuracy, speed, and robustness of technology used.
- Acceptability – degree of approval of a technology.
- Circumvention – ease of use of a substitute.
A biometric system can provide the following two functions:
- Verification – Authenticates its users in conjunction with a smart card, username or ID number. The biometric template captured is compared with that stored against the registered user either on a smart card or database for verification.
- Identification – Authenticates its users from the biometric characteristic alone without the use of smart cards, usernames or ID numbers. The biometric template is compared to all records within the database and a closest match score is returned. The closest match within the allowed threshold is deemed the individual and authenticated.
The main operations a system can perform are enrollment and test. During the enrollment, biometric information from an individual is stored. During the test, biometric information is detected and compared with the stored information. Note that it is crucial that storage and retrieval of such systems themselves be secure if the biometric system is to be robust. The first block (sensor) is the interface between the real world and the system; it has to acquire all the necessary data. Most of the times it is an image acquisition system, but it can change according to the characteristics desired. The second block performs all the necessary pre-processing: it has to remove artifacts from the sensor, to enhance the input (e.g. removing background noise), to use some kind of normalization, etc. In the third block features needed are extracted. This step is an important step as the correct features need to be extracted in the optimal way. A vector of numbers or an image with particular properties is used to create a template. A template is a synthesis of all the characteristics extracted from the source, in the optimal size to allow for adequate identifiability.
If enrollment is being performed the template is simply stored somewhere (on a card or within a database or both). If a matching phase is being performed, the obtained template is passed to a matcher that compares it with other existing templates, estimating the distance between them using any algorithm (e.g. Hamming distance). The matching program will analyze the template with the input. This will then be output for any specified use or purpose (e.g. entrance in a restricted area).
The following are used as performance metrics for biometric systems:
- False Accept Rate or False Match Rate (FAR or FMR) – the probability that the system incorrectly declares a successful match between the input pattern and a non-matching pattern in the database. It measures the percent of invalid matches. These systems are critical since they are commonly used to forbid certain actions by disallowed people.
- False Reject Rate or False Non-Match Rate (FRR or FNMR) – the probability that the system incorrectly declares failure of match between the input pattern and the matching template in the database. It measures the percent of valid inputs being rejected.
- Receiver Operating Characteristic or Relative Operating Characteristic (ROC) – In general, the matching algorithm performs a decision using some parameters (e.g. a threshold). In biometric systems the FAR and FRR can typically be traded off against each other by changing those parameters. The ROC plot is obtained by graphing the values of FAR and FRR, changing the variables implicitly. A common variation is the Detection error trade-off (DET), which is obtained using normal deviate scales on both axes. This more linear graph illuminates the differences for higher performances (rarer errors).
- Equal Error Rate or Crossover Error Rate (EER or CER) – the rate at which both accept and reject errors are equal. ROC or DET plotting is used because how FAR and FRR can be changed, is shown clearly. When quick comparison of two systems is required, the EER is commonly used. Obtained from the ROC plot by taking the point where FAR and FRR have the same value. The lower the EER, the more accurate the system is considered to be.
- Failure To Enroll Rate (FTE or FER) – the percentage of data input is considered invalid and fails to input into the system. Failure to enroll happens when the data obtained by the sensor are considered invalid or of poor quality.
- Failure To Capture Rate (FTC) – Within automatic systems, the probability that the system fails to detect a biometric characteristic when presented correctly.
- Template Capacity – the maximum number of sets of data which can be input into the system..
As the sensitivity of biometric devices increases, it decreases the FAR but increases the FRR.
One simple but artificial way to judge a system is by EER, but not all the authors provided it. Moreover, there are two particular values of FAR and FRR to show how one parameter can change depending on the other. For fingerprint there are two different results, the one from 2003 is older but it was performed on a huge set of people, while in 2004 far fewer people were involved but stricter conditions have been applied. For iris, both references belong to the same year, but one was performed on more people, the other one is the result of a competition between several universities so, even if the sample is much smaller, it could reflect better the state of art of the field.