Biometric data has well defined relationships between its various data elements: people, templates, samples and matches. An understanding of the associated structure is fundamental to both building robust biometric systems and the analysis of data relationships. However, many systems that are developed do not appropriately reflect these foundations, and as a consequence are less flexible than desired. The ongoing evolution of biometric standards is also helping to enforce data quality standards, and facilitate interoperability and data exchange between different biometric systems.

Every biometric has unique properties. In addition to the wide variety of physiological differences, the acquisition process introduces many differences in sample appearance and quality. Determining the variations that lead to poor performance is vital to the analysis of any biometric system. According to the Pareto principle, it is likely that 80% of problems in a system are due to just 20% of poor quality enrollments and acquisitions. Consequently, examining issues related to biometric data is useful and informative, as it gives an appreciation for real world challenges in deploying a biometric solution.

The goals of this chapter are to:
  • Explain the inherent relationships between people, templates, biometric data and matches (Sect. 3.1).

  • List some published biometric standards in data interchange, applications and testing (Sect. 3.2).

  • Show examples of quality variation for several commonly used biometrics (Sect. 3.3).


Face Recognition Facial Image Biometric System Iris Recognition Face Recognition System 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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