%Aigaion2 BibTeX export from Idiap Publications
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@ARTICLE{Anjos_IETBIOMETRICS_2013,
         author = {Anjos, Andr{\'{e}} and Chakka, Murali Mohan and Marcel, S{\'{e}}bastien},
       projects = {TABULA RASA},
          month = jul,
          title = {Motion-Based Counter-Measures to Photo Attacks in Face Recognition},
        journal = {Institution of Engineering and Technology Journal on Biometrics},
           year = {2013},
            url = {http://pypi.python.org/pypi/antispoofing.optflow},
       abstract = {Identity spoofing is a contender for high-security face recognition applications. With the advent of social media and globalized search, our 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 – we 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 three-fold: firstly we introduce a publicly available PHOTO-ATTACK database with associated protocols to measure the effectiveness of counter-measures. Based on the data available, we conduct a study on current state-of-the-art spoofing detection algorithms based on motion analysis, showing they fail under the light of these new dataset. By last, we 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 article.},
            pdf = {https://publications.idiap.ch/attachments/papers/2013/Anjos_IETBIOMETRICS_2013.pdf}
}