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 [BibTeX] [Marc21]
Motion-Based Counter-Measures to Photo Attacks in Face Recognition
Type of publication: Journal paper
Citation: Anjos_IETBIOMETRICS_2013
Publication status: Accepted
Journal: Institution of Engineering and Technology Journal on Biometrics
Year: 2013
Month: July
URL: http://pypi.python.org/pypi/an...
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.
Authors Anjos, André
Chakka, Murali Mohan
Marcel, Sébastien
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Total mark: 0
  • Anjos_IETBIOMETRICS_2013.pdf