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.

通过全面数据驱动的方法，本文提出了一种能够对虹膜图像进行合成的方法，能够表示不同瞳孔尺寸、非现有身份的虹膜图像，并在改变瞳孔尺寸时能够保持身份，相比于现有的线性和非线性虹膜变形模型，该方法在相似性和身份保持方面表现更好。