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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 _α MT _i 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 _α MT _i F features that process the input multi-spectral face image independently. These extracted features are then …