Affective and psychological perception are pivotal in human-machine interaction and essential domains within artificial intelligence. Existing physiological signal-based affective and psychological datasets primarily rely on contact-based sensors, …
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 …
For iris recognition in non-cooperative environments, iris segmentation has been regarded as the first most important challenge still open to the biometric community, affecting all downstream tasks from normalization to recognition. In recent years, …
For iris recognition in non-cooperative environments, iris segmentation has been regarded as the first most important challenge still open to the biometric community, affecting all downstream tasks from normalization to recognition. In recent years, …
Iris images captured in non-cooperative environments often suffer from adverse noise, which challenges many existing iris segmentation methods. To address this problem, this paper proposes a high-efficiency deep learning based iris segmentation …
Iris images captured in non-cooperative environments often suffer from adverse noise, which challenges many existing iris segmentation methods. To address this problem, this paper proposes a high-efficiency deep learning based iris segmentation …
In this paper, we present an end-to-end model, namely Seg-Edge bilateral constraint network. The iris edge map generated from rich convolutional layers optimize the iris segmentation by aligning it with the iris boundary. The iris region produced by the coarse segmentation limits the scope. It makes the edge filtering pay more attention to the interesting target. We compress the model while keeping the performance levels almost intact and even better by using l1-norm. The proposed model advances the state-of-the-art iris segmentation accuracies.