Presentation attack detection using Laplacian decomposed frequency response for visible spectrum and Near-Infra-Red iris systems

Biometrics systems are being challenged at the sensor level using artefact presentation such as printed artefacts or electronic screen attacks. In this work, we propose a novel technique to detect the artefact iris images by decomposing the images into Laplacian pyramids of various scales and obtain frequency responses in different orientations. The obtained features are classified using a support vector machine with a polynomial kernel. Further, we extend the same technique with majority voting rule to provide the decision on artefact detection for video based iris recognition in the visible spectrum. The proposed technique is evaluated on the newly created visible spectrum iris video database and also Near-Infra-Red (NIR) images. The newly constructed visible spectrum iris video database is specifically tailored to study the vulnerability of presentation attacks on visible spectrum iris recognition using videos on a smartphone. The newly constructed database is referred as `Presentation Attack Video Iris Database' (PAVID) and consists of 152 unique iris patterns obtained from two different smartphone - iPhone 5S and Nokia Lumia 1020. The proposed technique has provided an Attack Classificiation Error Rate (ACER) of 0.64% on PAVID database and 1.37% on LiveDet iris dataset validating the robustness and applicability of the proposed presentation attack detection (PAD) algorithm in real life scenarios.