With biometrics playing the role of a password which can not be replaced if stolen, the necessity of establishing countermeasures to biometric spoofing attacks has been recognized. Regardless of the biometric mode, the typical approach of anti-spoofing systems is to classify biometric evidence based on features discriminating between real accesses and spoofing attacks. For the first time, to the best of our knowledge, this paper studies the amount of client-specific information within these

more »

... ures and how it affects the performance of anti-spoofing systems. We make use of this information to build two client-specific anti-spoofing solutions, one relying on a generative and another one on a discriminative paradigm. The proposed methods, tested on a set of state-of-the-art antispoofing features for the face mode, outperform the clientindependent approaches with up to 50% relative improvement and exhibit better generalization capabilities on unseen types of spoofing attacks.