Deep metric learning method for open-set iris recognition

The existing iris recognition methods offer excellent recognition performance for known classes, but they do not perform well when faced with unknown classes. The process of identifying unknown classes is referred to as open-set recognition. To improve the robustness of iris recognition system, this work integrates a hash center to construct a deep metric learning method for open-set iris recognition, called central similarity based deep hash. It first maps each iris category into defined hash centers using a generation hash center algorithm. Then, OiNet is trained to each iris texture to cluster around the corresponding hash center. For testing, cosine similarity is calculated for each pair of iris textures to estimate their similarity. Based on experiments conducted on public datasets, along with evaluations of performance within the dataset and across different datasets, our method demonstrates substantial performance advantages compared with other algorithms for open-set iris recognition.