Open-set iris recognition based on deep learning

Abstract

The existing iris recognition methods offer excellent recognition performance for known classes, but they do not consider the rejection of unknown classes. It is important to reject an unknown object class for a reliable iris recognition system. This study proposes open-set iris recognition based on deep learning. In the method, by training the deep network, the extracted iris features are clustered near the feature centre of each kind of iris image. Then, the authors build an open-class features outlier network (OCFON) containing distance features, which maps the features extracted by the deep network to a new feature space and classifies them. Finally, the unknown class samples are determined by a SoftMax probability threshold. The authors conducted experiments on the open iris dataset constructed using the iris datasets CASIA-Iris-Twins and CASIA-Iris-Lamp. The experiment shows that the proposed method has good open-set iris recognition performance, can effectively distinguish iris samples of unknown classes, and has little impact on the recognition ability of known classes of iris samples.