Biometric Recognition Challenges and Opportunities (2010) / Chapter Skim
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3 Lessons from Other Large-Scale Systems
3 Lessons from Other Large-Scale Systems
Pages 76-84
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From page 76... ...
Common characteristics of successful deployments include good project management and definition of goals, alignment of biometric capabilities with the underlying need and operational environment, and a thorough threat and risk analysis of the system under consideration. Common contributors to failures include the following: • Inappropriate technology choices, • Lack of sensitivity to user perceptions and requirements, • Presumption of a problem that does not exist, • Inadequate surrounding support processes and infrastructure, • Inappropriate application of biometrics where other technologies would better solve the problem, • Lack of a viable business case, and • Poor understanding of population issues, such as variability among those to be authenticated or identified.
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From page 77... ...
Production-line systems have been studied systematically since before World War II from the perspectives of industrial engineering, statistics, experimental design, operations research, and quality control. Insights gained from the study of such systems have been generalized to better understand and improve the performance of systems for product development and other industrial processes and to facilitate improvements in corporate management.
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From page 78... ...
In the case of a production line, for example, a requirement might be for the sensor to respond reliably and repeatedly only to stimuli in the desired range and measure stimuli accurately, under conditions in the production environment. The range of stimuli, sensor sensitivity and resolution, and resistance to environmental disturbances must be accurately specified during the design process in order for the sensor to properly identify defective units, which is its ultimate purpose.
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From page 79... ...
Although dramatic but relatively brief slumps and streaks are a major source of dis cussion by sports analysts and some stock traders, basing major decisions on such brief events rarely leads to prosperity for a baseball team or an investor. A deeper level of understanding develops from the awareness that random variation in output typically comes from multiple sources that persist even as its momentary influences fluctuate.
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From page 80... ...
There is substantial precedent for such challenge experiments in other contexts, including evaluations of Internal Revenue Service tax assistance and Transportation Security Administration airport passenger and baggage screening. So, independent of the particular biometric modality and its applica tion, the following lessons can be drawn from the experience and methodologies that have evolved in industrial production: • System objectives must be clarified at the outset if the system is to be designed efficiently and if the ability to evaluate system performance is to be preserved.
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From page 81... ...
attack modes, are the best way to identify the potential for such errors and ways to prevent them. Erroneous rejections of true recognition claims and erroneous acceptances of false claims should be documented and subject to rigorous fault analysis, just as would take place in the case of an investigation into a transportation crash.
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From page 82... ...
• Limitations of individual components can vitiate the effectiveness of other components. For instance, in the system described above, a pathologist who cannot detect true prostate cancer renders the accuracy of earlier components in the sequence virtually irrelevant.
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From page 83... ...
• A poor adjudication process, or an ineffective backup process for dealing with failures-to-acquire (see Chapter 2) in a biometric system, may negate the benefits of good error rates in the basic biometric technology.
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From page 84... ...
Extrapolation of technological or system performance characteristics across settings or challenges -- for example, from (1) laptop access control to auto theft control to border control or (2)
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