Adaptive biometric systems that can improve with use

Abstract

Performances of biometric recognition systems can degrade quickly when the input biometric traits exhibit substantial variations compared to the templates collected during the enrollment stage of the system's users. On the other hand, a lot of new unlabelled biometric data, which could be exploited to adapt the system to input data variations, are made available during the system operation over the time. This chapter deals with adaptive biometric systems that can improve with use by exploiting unlabelled data. After a critical review of previous works on adaptive biometric systems, the use of semisupervised learning methods for the development of adaptive biometric systems is discussed. Two examples of adaptive biometric recognition systems based on semisupervised learning are presented in the chapter, and the concept of biometric co-training is introduced for the first time. © 2008 Springer London.