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.