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 EyePreserve, a novel, fully data-driven non-linear texture deformation model, embedded within an identity-preserving, pupil size-varying synthesis framework. 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 (biomechanical-based) iris deformation models. Two immediate applications of the proposed approach are: (a) synthesis of new, or enhancement of the existing datasets of same-identity iris images with varying pupil sizes, with correct modeling of complex iris texture deformations, and (b) helping forensic human experts examine iris image pairs with significant differences in pupil dilation by deforming one or both images to align the pupil size. 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.

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