Race Bias Analysis of Bona Fide Errors in face anti-spoofing

Face recognition is the method of choice behind some of the most widely deployed biometric authentication systems, currently supporting a range of applications, from passport control at airports, to mobile phone or laptop login. A key weaknesses of the technology, preventing it from being employed in security sensitive applications in uncontrolled environments, as for example ATM machines for money withdrawal, is its vulnerability to presentation attacks, where imposters attempt to gain wrongful access by presenting in front of the system's camera a photo, or a video, or by wearing a mask resembling a registered person. As a solution to this problem, algorithms for presentation attack detection (PAD) are developed, that is, binary classifiers trained to distinguish between the bona fide samples coming from live subjects, and those coming from imposters. The large variety in the types of possible presentation attacks, and the large variation in the environmental conditions under which they might take place, make PAD a particularly challenging problem. However, the current state-of-the-art, utilising the power of deep learning, comprises classifiers with excellent accuracy rates, and a satisfactory generalisation power to at least a limited number of previously unseen attacks. Cross-database generalisation is still problematic, however, it is debatable if this is a real obstacle to the deployment of PAD algorithms in practical applications, since such algorithms as usually embedded in specific face recognition systems, with given camera specifications and configurations. Here, we deal with the problem of race bias in face anti-spoofing algorithms. It is a topic that has attracted considerably less research interest than accuracy and generalisation power, despite the fact that it raises ethical, legal, and regulatory considerations, which, by their own, can prevent adoption in specific applications. Addressing this gap, the aim of this paper is to provide a framework for studying the question: Does the classifier work equally well on people from all races?.