Abstract
Biometric person recognition poses a very challenging pattern recognition problem because of large variability in biometric sample quality encountered during testing and a restricted number of enrollment samples for training. Furthermore, biometric traits can change over time due to aging and change of lifestyle. Effectively, the noise factors encountered in testing cannot be represented by the limited training samples . A promising solution to training data deficiency and ageing is to use an adaptive biometric system. These systems attempt to adapt themselves to follow the change in the input biometric data. Adaptive biometrics deserves a treatment on its own right because standard machine-learning algorithms cannot readily handle changing signal quality. The aim of this chapter is to introduce the concept of adaptive biometric systems in terms of taxonomy, level of adaptation, open issues and challenges involved.
Keywords
- Adaptive Biometric Systems
- Challenging Pattern Recognition Problem
- Input Biometric Data
- Biometric Sample
- Stringent Statistical Threshold
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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Notes
- 1.
A template refers to the biometric sample used for enrollment. The term “model” refers to statistical representation derived from one or more biometric samples. In order for our discussion to cover both types of methods, we shall adapt the standard vocabulary, that is, “biometric reference” or simply reference. A reference is subsequently used for comparing a biometric test/query sample to obtain a similarity score.
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Rattani, A. (2015). Introduction to Adaptive Biometric Systems. In: Rattani, A., Roli, F., Granger, E. (eds) Adaptive Biometric Systems. Advances in Computer Vision and Pattern Recognition. Springer, Cham. https://doi.org/10.1007/978-3-319-24865-3_1
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