A Deep Learning Based Approach to Iris Sensor Identification

An efficient iris sensor identification algorithm can be used in certain forensic applications, i.e. detecting mislabeled iris data at large scale iris datasets, and verifying the validity of the data origin of collected iris datasets that are available to be shared. Such knowledge can potentially increase the overall iris recognition system accuracy by offering the operator the option to match same-sensor or cross-sensor iris images. In either case the knowledge of the origin of the sensor used to collect these data, when not available, or the correction of mislabeled data, is expected to result in higher iris matching accuracy. Another benefit of iris sensor identification is that it can assist in improving the detection of fake iris data, i.e. when knowing the iris sensor, we can apply more appropriate models for fake detection that are tuned for a specific iris sensor. In this paper we propose an efficient deep learning-based iris recognition algorithm that is sensor inter-operable. Our approach utilizes a moderate amount of data and is adaptable to learning rate variations as well as variations of the amount of data used for training per class. Our proposed approach uses a set of iris datasets that include iris images captured at different standoff distances. We are using the original captured, dual eye, or periocular images rather than the iris itself, after detecting, segmenting, and normalizing the iris. Thus, the algorithm is efficient, fast, and less depended on additional algorithmic processes that can add computational complexity. Our proposed process includes transfer learning using iris images of higher quality via the utilization of a set of image quality metrics and achieves close to a hundred percent accuracy after cross-validation.