Face presentation attack detection across spectrum using time-frequency descriptors of maximal response in Laplacian scale-space

Multi-spectral face recognition has been an active area of research over the past few decades. However, the vulnerability of multi-spectral face recognition systems is a growing concern that argues the need for Presentation Attack Detection (PAD) (or countermeasure or anti-spoofing) schemes to successfully detect targeted attacks. In this work, we present a novel feature descriptor L
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F that can effectively capture time-frequency features from the maximum response obtained on the high pass band image, which is obtained from the scale-space decomposition of the presented image. The proposed feature descriptor can effectively capture the micro-texture patterns that can be effectively used describe the variation from the presented image. We then propose a new framework using the proposed L
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F features that process the input multi-spectral face image independently. These extracted features are then classified using a linear Support Vector Machine (SVM) to obtain the binary decision. Finally, we carry out a decision fusion using the And rule to obtain the final decision. Extensive experiments are carried out on publicly available multi-spectral face datasets that have indicated the efficacy of the proposed scheme.