🤖 AI Summary
To address the challenges of modeling nonlinear texture deformation and preserving identity under pupillary scaling in iris images, this paper proposes the first end-to-end, purely data-driven framework for synthesizing pupil-size-variable iris images. Methodologically, it integrates a segmentation-mask-guided nonlinear deformation module with explicit identity-consistency constraints, eschewing conventional linear or biomechanical modeling assumptions to accurately capture complex texture distortions induced by iris muscle contraction. The synthesized images comply with ISO/IEC 29794-6 quality standards and enable arbitrary pupil-size synthesis for both real and synthetic identities. In downstream iris recognition, the method significantly improves matching similarity between same-identity samples across varying pupil sizes, outperforming existing state-of-the-art approaches. Source code and pretrained models are publicly available.
📝 Abstract
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.
