Adaptive biometric systems that can improve with use 1 Fabio Roli, Luca Didaci, Gian Luca Marcialis Department of Electrical and Electronic Engineering – University of Cagliari Piazza d’Armi – I-09123 Cagliari (Italy) {roli, luca.didaci, marcialis}@diee.unica.it Abstract. Performances of biometric recognition systems can degrade quickly when the input biometric traits exhibit substantial variations compared to the templates collected during the enrolment stage of 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 semi-supervised learning methods for the development of adaptive biometric systems is discussed. Two examples of adaptive biometric recognition systems based on semi-supervised learning are presented along the chapter, and the concept of biometric co-training is introduced for the first time. 1 Introduction Computerized recognition of the personal identity using biometric traits, such as fingerprints and faces, is receiving an increasing attention from the academic and industrial communities, due to both the variety of its applications and the many open issues which make the performances of current systems still far from the ones of the humans (Sinha et al. 2006a; Sinha et al. 2006b). A typical biometric recognition system operates in two distinct stages: the enrolment stage and the recognition, or identification, stage (Ross et al. 2006). In the enrolment stage, for each system’s user, a biometric trait (e.g., a fingerprint image) is acquired and processed to repre- sent it with a feature set (e.g., minutiae points). This enrolled feature set, labeled with the user’s identity, is named template and is stored as a prototype of user’s biometric 1 The title of this manuscript was inspired by a George Nagy’s paper (Nagy 2004a).