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Face spoofing detection based on colour distortions

Face spoofing detection based on colour distortions

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Securing face recognition systems against spoofing attacks have been recognised as a real challenge. Spoofing attacks are conducted by printing or displaying a digital acquisition of a capture subject (target user) in front of the sensor. These extra reproduction stages generate colour distortions between face artefacts and real faces. In this work, the problem of spoof detection is addressed by modelling the radiometric distortions generated by the recapturing process. The spoof detection process takes advantage of enrolment data and occurs after face identification so that for each client the authors have at disposal at least one genuine face sample as a reference. Once identified, they compute the colour transformation between the observed face and its enrolment counterpart. A compact parametric representation is proposed to model those radiometric transforms and it is used as features for classification. They evaluate the proposed method on Replay-Attack, CASIA and MSU public databases and show its competitiveness with state-of-the-art countermeasures. Limitations of the proposed method are clearly identified and discussed through experiments in adversary evaluation conditions where colour distortions are not only generated by the recapturing process but also by natural illumination variations.

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