Synthesis of same-identity biometric iris images, both for existing and non-existing identities while preserving the identity across a wide range of pupil sizes, is complex due to the intricate iris muscle constriction mechanism, requiring a precise model of iris non-linear texture deformations to be embedded into the synthesis pipeline. This paper presents the first method of fully data-driven, identity-preserving, pupil size-varying synthesis of iris images. This approach is capable of synthesizing images of irises with different pupil sizes representing non-existing identities, as well as non-linearly deforming the texture of iris images of existing subjects given the segmentation mask of the target iris image. Iris recognition experiments suggest that the proposed deformation model both preserves the identity when changing the pupil size, and offers better similarity between same-identity iris samples with significant differences in pupil size, compared to state-of-the-art linear and non-linear (bio-mechanical-based) iris deformation models. Two immediate applications of the proposed approach are: (a) synthesis of, or enhancement of the existing biometric datasets for iris recognition, mimicking those acquired with iris sensors, and (b) helping forensic human experts examine iris image pairs with significant differences in pupil dilation. Images considered in this work conform to selected ISO/IEC 29794-6 quality metrics to make them applicable in biometric systems. The source codes and model weights are offered with this paper.

翻译：合成相同身份的生物特征虹膜图像（包括现有身份与虚构身份），并在大范围瞳孔尺寸变化下保持身份一致性，是一项复杂任务。这源于虹膜肌肉收缩机制的复杂性，需要将虹膜非线性纹理形变的精确模型嵌入合成流程。本文提出了首个完全数据驱动、身份保持、瞳孔尺寸可变的虹膜图像合成方法。该方法能够合成代表虚构身份、具有不同瞳孔尺寸的虹膜图像，同时也能根据目标虹膜图像的分割掩码，对现有受试者的虹膜图像纹理进行非线性形变。虹膜识别实验表明，与最先进的线性和非线性（基于生物力学）虹膜形变模型相比，所提出的形变模型在改变瞳孔尺寸时既能保持身份一致性，又能显著提升瞳孔尺寸差异较大的同身份虹膜样本间的相似度。该方法的两个直接应用场景包括：（a）合成或增强现有虹膜识别生物特征数据集，模拟虹膜传感器采集的数据；（b）协助司法人类专家检验瞳孔扩张程度差异显著的虹膜图像对。本研究涉及的图像均符合选定的ISO/IEC 29794-6质量标准，确保其适用于生物特征系统。本文同步提供了源代码与模型权重。