Motion-based counter-measures to photo attacks in face recognition

Identity spoofing is a contender for high-security face-recognition applications. With the advent of social media and globalised search, peoples face images and videos are wide-spread on the Internet and can be potentially used to attack biometric systems without previous user consent. Yet, research to counter these threats is just on its infancy - the authors lack public standard databases, protocols to measure spoofing vulnerability and baseline methods to detect these attacks. The contributions of this work to the area are 3-fold: first, the authors a publicly available PHOTO-ATTACK database with associated protocols to measure the effectiveness of counter-measures is introduced. Based on the data available, a study is conducted on current state-of-the-art spoofing detection algorithms based on motion analysis, showing they fail under the light of this new dataset. By last, the authors propose a new technique of counter-measure solely based on foreground/background motion correlation using optical flow that outperforms all other algorithms achieving nearly perfect scoring with an equal-error rate of 1.52% on the available test data. The source code leading to the reported results is made available for the replicability of findings in this study.