Segmentation-based ID preserving iris synthesis using generative adversarial networks

This study proposes a method for generating ID preserving synthetic iris database. The proposed method can be applied in the generation of a synthetic iris database for various iris recognition tasks. This work successfully combines the main idea of generative adversarial learning, segmentation, and identification to solve real-world problems. The method produces synthetic iris images from the segmentation masks given ID information. The segmentation mask, iris pose, is devised from the input image by using a segmentation network. By doing this, the ID-preserving iris synthesis method generates an unlimited number of synthetic iris images by processing the provided input images. The accuracy of the generated iris images is validated by measuring top-1, top-5, and Area under the Curve (AUC). The SegNet and IDNet performance was evaluated using class accuracy in terms of precision, recall, and F1-score alongside the computation model complexity. This study exhibits ease of use, compatibility, and accuracy in preserving ID information for the generated synthetic images compared to the other baseline methods. Evaluation results prove the efficacy of this work by comparing the randomly generated iris images using the current study alongside existing methods.