On the Generalisation Capabilities of Fisher Vector based Face Presentation Attack Detection

• URL: http://arxiv.org/abs/2103.01721v1
• Date: Tue, 2 Mar 2021 13:49:06 GMT
• Title: On the Generalisation Capabilities of Fisher Vector based Face Presentation Attack Detection
• Authors: L\'azaro J. Gonz\'alez-Soler, Marta Gomez-Barrero, Christoph Busch
• Abstract summary: Face Presentation Attack Detection techniques have reported a good detection performance when they are evaluated on known Presentation Attack Instruments. In this work, we use a new feature space based on Fisher Vectors, computed from compact Binarised Statistical Image Features histograms. This new representation, evaluated for challenging unknown attacks taken from freely available facial databases, shows promising results.
• Score: 13.93832810177247
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In the last decades, the broad development experienced by biometric systems has unveiled several threats which may decrease their trustworthiness. Those are attack presentations which can be easily carried out by a non-authorised subject to gain access to the biometric system. In order to mitigate those security concerns, most face Presentation Attack Detection techniques have reported a good detection performance when they are evaluated on known Presentation Attack Instruments (PAI) and acquisition conditions, in contrast to more challenging scenarios where unknown attacks are included in the test set. For those more realistic scenarios, the existing algorithms face difficulties to detect unknown PAI species in many cases. In this work, we use a new feature space based on Fisher Vectors, computed from compact Binarised Statistical Image Features histograms, which allow discovering semantic feature subsets from known samples in order to enhance the detection of unknown attacks. This new representation, evaluated for challenging unknown attacks taken from freely available facial databases, shows promising results: a BPCER100 under 17% together with an AUC over 98% can be achieved in the presence of unknown attacks. In addition, by training a limited number of parameters, our method is able to achieve state-of-the-art deep learning-based approaches for cross-dataset scenarios.

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