With biometrics playing the role of a password which cannot 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 antispoofing systems is to classify the 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 antispoofing systems. We make use of this information to build two client-specific antispoofing 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 client-independent approaches with up to 50% relative improvement and exhibit better generalization capabilities on unseen types of spoofing attacks.

While more accurate and reliable than ever, the trustworthiness of biometric verification systems is compromised by the emergence of spoofing attacks. Responding to this threat, numerous research publications address isolated spoofing detection, resulting in efficient counter-measures for many biometric modes. However, an important, but often overlooked issue regards their engagement into a verification task and how to measure their impact on the verification systems themselves. A novel evaluation framework for verification systems under spoofing attacks, called expected performance and spoofability framework, is the major contribution of this paper. Its purpose is to serve for an objective comparison of different verification systems with regards to their verification performance and vulnerability to spoofing, taking into account the system’s application-dependent susceptibility to spoofing attacks and cost of the errors. The convenience of the proposed open-source framework is demonstrated for the face mode, by comparing the security guarantee of four baseline face verification systems before and after they are secured with antispoofing algorithms.