Cross-sensor iris spoofing detection using orthogonal features. (January 2019) - Kaur, Bineet
Abstract: Iris recognition systems face issues related to spoofing attacks that obfuscate the natural iris texture pattern. This makes it a challenging problem to be used for large-scale high security applications. The paper introduces a rotation-invariant feature-set comprising of Zernike moments and Polar harmonic transforms that extract local intensity variations for detection of iris spoofing attacks. Experimental results have been implemented on four publicly available iris spoofing databases: IIITD Contact Lens, IIITD Iris Spoofing, Clarkson LivDet-Iris 2015 and Warsaw LivDet-Iris 2015 that include both contact lens and print attack spoofing samples. Experiments demonstrate that the proposed system easily detects spoofing attacks even when acquired using multiple sensors.