An Introduction to Evaluating Biometric Systems

Evaluating Biometrics

Bio-metric industry has tremendous growth since 3 to 5 years. There was a call for more bio-metric application after the collapse of twin towers on sep 11th. Those, however, seem to be reluctant to invest in a technology that is still emerging and therefore features a bewildering diversity of products and companies. As a recent article in the Washington Business Journal brought it to the point, “bio-metrics is the next evolution in security, but it’s a very fragmented industry, with lots of companies trying different things” 

Metrics and performance criteria 

It’s always challenging to any bio-metric or traditional authentication systems to provide more secure authentication, identification and verification process. There are lot of mechanisms by which the performance of any system is monitored. All these values should be tracked in a finniest manner irrespective of the methodologies used while capturing the results.

All most all the bio-metric tools uses few buzz words to identify the measuring the factors of the performance. For example, a renowned vendor of fingerprint sensors claims on his website that his “sensor achieved 0.0% false acceptance and 0.0% false rejection rates”. Like this there are lot of attractive and good looking technical standards and measurements are set to measure the performance of the bio-metric systems. There are many metric that we used to measure the performance of the bio-metric system and three among them are as listed below 

False Match Rates (FMR) 

False Match Rate is the probability that the respective system will match the current user verification template with another user verification and enrollment template. It can be understood as the likelihood of an impostor being recognized as a legitimate user. This is the most frequently found error rate and can be handled very critically with any knowledge on the error rates. 

False Non Match Rate (FNMR) 

False Non Match Rate is the probability that a particular user verification template is not matched with the same users enrollment template. Heavy  false match rates could always lead to low or zero productivity or user frustration and may be not that critical as False Match Rate. 

Failure-to-Enroll Rate (FTE) 

Failure-to-Enroll Rate is the probability that the system will not

be able to extract distinctive, consistent and replaceable characteristics from the sample presented during the enrollment process, i.e. this is the likelihood of the system being unable to create an enrollment template for a new user. The affect of this could in the behavioral side rather than physical side. 

All the above Failure rates will impact the system in one or more ways and this affect is depend on the number of iterations worked on the failure rates. The most predominant among all the three key failures are are False Non Match Rate and Failure-to-Enroll. These two plays a very important role in determining the performance of the bio-metric system. In the ideal cases at maximum only three attempts should be done for any metrics and it’s affect operations.

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