Iris Recognition on Mobile Devices Using Near-Infrared Images

Abstract Iris recognition provides a promising solution to enhance the security of private data on mobile devices. This chapter first analyzes the problems of iris recognition on mobile devices and then introduces our attempts to solve these problems by super-resolution, feature learning and multimodal biometrics fusion. Two super-resolution methods, which are based on convolutional neural networks (CNNs) and random forests, respectively, are compared experimentally. A pairwise CNNs model is proposed to automatically learn the correlation features between two iris images. Experimental results show that the learned features are highly complementary to local features, e.g., ordinal measures. Finally, we discuss the fusion of iris, periocular, and facial information which is a natural and effective way to improve the overall performance.

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