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Biometric Recognition: Challenges and Opportunities
seems appropriate, given their scale and scope, to consider whether the biometrics community can learn lessons from large-scale systems that have been deployed in other domains. This chapter explores some of the technical/engineering and societal lessons learned from large-scale systems in manufacturing and medical screening and diagnosis. In each case, the discussion points out useful analogies to biometric systems and applications.
Manufacturing systems convert initial materials into finished products that must meet quality specifications. Each step in the conversion may consist of a complex process sensitive to multiple characteristics of the input materials and processing conditions. Each step also represents an economic investment; modifications to the process that can achieve equal or higher quality at lower cost are every company’s goal. 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.
A simple example, used in a 2005 briefing to the study committee by Lynne Hare of Kraft, Incorporated, is the development of a new sensor for a manufacturing production line. The process begins with identifying the business need for the sensor and proceeds through its implementation and then deployment in the production line. The stages include explicit translation of the business need into the scientific requirements for the sensor, fabrication of a prototype sensor, preliminary (static) testing, formal static and dynamic testing, pilot installation and testing, and production line implementation and validation. The process never ends, because revalidation is scheduled at periodic intervals. At each stage of testing and data collection, the information obtained may send the development process back to an earlier stage to correct any observed deficiencies and improve robustness of the sensor to varying conditions.
This example can be interpreted directly or as analogy. Directly, it gives a model for developing and implementing devices required by any biometric system to sense biometric traits, for example, fingerprint scanners, iris scanners, and audio recorders. There is also an analogy between development and validation of a sensor and the development and implementation of a biometric system. In this analogy, the multiple levels of testing—preliminary static, formal static and dynamic, and production line testing—are counterparts to technology, scenario, and operational