Iris images captured in less-constrained environments, especially at long distances often suffer from the interference of low resolution, resulting in the loss of much valid iris texture information for iris recognition. In this paper, we propose a …
The iris patterns of the human contain a large amount of randomly distributed and irregularly shaped microstructures. These microstructures make the human iris informative biometric traits. To learn identity representation from them, this paper …
As biometric data undergo rapidly growing privacy concerns, building large-scale datasets has become more difficult. Unfortunately, current iris databases are mostly in small scale, e.g., thousands of iris images from hundreds of identities. What's …
Towards Interpretable Defense Against Adversarial Attacks via Causal Inference, FedIris: Towards More Accurate and Privacy-preserving Iris Recognition via Federated Template Communication
Towards More Discriminative and Robust Iris Recognition by Learning Uncertain Factors, Multitask deep active contour-based iris segmentation for off-angle iris images
The uncontrollable acquisition process limits the performance of iris recognition. In the acquisition process, various inevitable factors, including eyes, devices, and environment, hinder the iris recognition system from learning a discriminative …
Iris recognition has been considered as a secure and reliable biometric technology. However, iris images are prone to off-angle or are partially occluded when captured with fewer user cooperations. As a consequence, iris recognition especially iris …
Cross-spectral recognition is still an open challenge in iris recognition. In cross-spectral iris recognition, there exist distinct device-specific bands between near-infrared (NIR) and visible (VIS) images, resulting in the distribution gap between …