access icon free 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.

Inspec keywords: image motion analysis; face recognition; image sequences; authorisation

Other keywords: background motion correlation; foreground motion correlation; equal-error rate; motion-based photo attack counter-measures; source code; optical flow; spooling vulnerability measurement protocols; public standard databases; social media; globalised search; baseline methods; face videos; biometric system attack; spooling detection algorithms; face images; publicly available photo-attack database; high-security face-recognition applications

Subjects: Data security; Computer vision and image processing techniques; Image recognition; Image and video coding; Video signal processing

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