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 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 s ynthesis 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 not only preserves the identity when changing the pupil size but 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 in examining iris image pairs with significant differences in pupil dilation. Source codes and weights of the models are made available with the paper.

翻译：针对同一身份的生物特征虹膜图像合成，无论是为现有身份还是非现有身份，在保持身份一致性的同时覆盖多种瞳孔尺寸，其复杂度源于虹膜肌肉收缩机制的复杂性，需在合成流程中嵌入虹膜非线性纹理变形的精确模型。本文首次提出完全数据驱动、身份保持且瞳孔尺寸可变的虹膜图像合成方法。该方法不仅能合成代表非现有身份且具有不同瞳孔尺寸的虹膜图像，还能基于目标虹膜图像的分割掩膜，对现有受试者的虹膜图像纹理进行非线性变形。虹膜识别实验表明，与当前最优的线性及非线性（基于生物力学）虹膜变形模型相比，本文提出的变形模型不仅能在改变瞳孔尺寸时保持身份特征，还能使相同身份且瞳孔尺寸差异显著的虹膜样本间获得更高的相似度。该方法具有两项直接应用：（a）合成或增强现有虹膜识别生物特征数据集，模拟虹膜传感器获取的图像；（b）辅助法医学专家检验瞳孔扩张程度差异显著的虹膜图像对。论文附有模型的源代码及权重文件。