With biometrics playing the role of a password which can not be replaced if stolen, the necessity of establishing counter- measures 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 features 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 anti- spoofing features for the face mode, outperform the client- independent approaches with up to 50% relative improvement and exhibit better generalization capabilities on unseen types of spoofing attacks.