Comparison of elliptical unwrapping and frontal projection for off-angle iris normalization

Emily Ehrlich; Mahmut Karakaya

doi:10.1117/12.2588898

12 April 2021 Comparison of elliptical unwrapping and frontal projection for off-angle iris normalization

Emily Ehrlich, Mahmut Karakaya

Proceedings Volume 11751, Disruptive Technologies in Information Sciences V; 117510O (2021) https://doi.org/10.1117/12.2588898
Event: SPIE Defense + Commercial Sensing, 2021, Online Only

Abstract

Iris recognition is one of the most accurate biometric recognition techniques, however off-angle iris recognition has yet to have an established comprehensive recognition framework. This is due to the difficulties in the recognition of off-angle iris image inconsistencies within the iris patterns when gaze deviations are present. In this work, we investigate different iris normalization techniques and compare their performance. The two methods under investigation include elliptical normalization and circular normalization after frontal projection of off-angle iris recognition. Elliptical normalization samples the iris texture using elliptical segmentation parameters: 𝑥, 𝑦, 𝑟₁ , 𝑟₂ , θ where 𝑥, 𝑦 are coordinates, 𝑟₁, 𝑟₂ are the radius, and θ is the orientation. Also, when investigating circular unwrapping, we will be using the ellipse segmentation parameters to estimate the gaze deviation. The image will be projected back to a frontal view using perspective transformation. Then, we segment the transformed image and normalize using the circular parameters: 𝑥, 𝑦, 𝑟 where 𝑥, 𝑦 are coordinates and r is the radius. We further investigate if: (i) elliptical normalization or circular unwrapping recognition performance is higher, and (ii) if the two segmentation methods in circular unwrapping increase the recognition efficiency

Conference Presentation

Citation Download Citation

Emily Ehrlich and Mahmut Karakaya "Comparison of elliptical unwrapping and frontal projection for off-angle iris normalization", Proc. SPIE 11751, Disruptive Technologies in Information Sciences V, 117510O (12 April 2021); https://doi.org/10.1117/12.2588898

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