EyePreserve: Identity-Preserving Iris Synthesis
Siamul Karim Khan, Patrick Tinsley, Mahsa Mitcheff, Patrick Flynn,, Kevin W. Bowyer, Adam Czajka

TL;DR
This paper introduces a novel data-driven method for synthesizing iris images that preserves identity across varying pupil sizes, improving biometric dataset augmentation and forensic analysis.
Contribution
It presents the first fully data-driven iris synthesis approach that models non-linear texture deformations for different pupil sizes, maintaining identity consistency.
Findings
The deformation model preserves identity across pupil size changes.
Synthesized images improve iris recognition accuracy.
Method outperforms existing deformation models in identity preservation.
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…
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Taxonomy
TopicsBiometric Identification and Security · Forensic Fingerprint Detection Methods · Forensic and Genetic Research
