Iris Anti-Spoofing Solution for Mobile Biometric Applications

Abstract

The ability to provide reliable protection against counterfeiting is one of the key requirements for a biometric security system. Iris recognition as the technology emerging on mobile market is assumed to handle various types of spoof attacks to prevent compromise of the user’s personal data. A method of iris anti-spoofing is proposed in this work. It is based on applying of convolutional neural network and capable to work in real-time on the mobile device with highly limited computational resources. Classification of iris sample for spoof and live is made by a single frame using a pair of images: eye region and normalized iris. The following types of iris spoof samples are considered in this particular work: printed on paper, printed on paper with imposition of a contact lens, printed on paper with application of transparent glue. Testing of the method is performed on the dataset manually collected and containing all the mentioned spoof sample types. The method revealed its high performance in both classification accuracy and processing speed as well as robustness under uncontrollably changing environmental conditions, which are specific and significant when interacting with the mobile device.