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IRINA: Iris Recognition (Even) in Inaccurately Segmented Data

CVPR 2017 · Detection and segmentation › Open-set and few-shot detection · approach: Industrial anomaly detection · 34 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Use IRINA instead of Yang et al.Iris recognition · CASIA-IrisV3-LampAUC: 0.995 ± 4e−4 -> 0.999 ± 1e−4Helped
Use IRINA instead of Yang et al.Iris recognition · CASIA-IrisV4-LampAUC: 0.993 ± 5e−4 -> 0.995 ± 0.002Helped
Use IRINA instead of Yang et al.Iris recognition · CASIA-IrisV4-ThousandAUC: 0.988 ± 6e−4 -> 0.996 ± 0.001Helped
Use IRINA instead of Yang et al.Iris recognition · WVUAUC: 0.980 ± 0.001 -> 0.991 ± 0.002Helped
Use IRINA instead of Yang et al.Iris recognition · CASIA-IrisV3-LampEER: 0.021 ± 0.004 -> 0.006 ± 0.001Helped
Use IRINA instead of Yang et al.Iris recognition · CASIA-IrisV4-LampEER: 0.028 ± 0.004 -> 0.026 ± 0.005Helped
Use IRINA instead of Yang et al.Iris recognition · CASIA-IrisV4-ThousandEER: 0.045 ± 0.004 -> 0.030 ± 0.005Helped
Use IRINA instead of Yang et al.Iris recognition · WVUEER: 0.065 ± 0.008 -> 0.042 ± 0.008Helped
Use IRINA rather than Yang et al. and Sun and Tan under segmentation errorsIris recognition · CASIA-IrisV4-Thousand with inaccurate segmentationAUCHelped
Use IRINA rather than Belcher and Du under segmentation errorsIris recognition · CASIA-IrisV4-Thousand with inaccurate segmentationAUCHelped
Set α below 0.9, reducing local deformation-field variation (ablation)IRINA · CASIA-IrisV3-LampAUCHurt

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