Best Practices in Reporting Iris Recognition Results

Popescu-Bodorin, Nicolaie; Balas, Valentina E.

doi:10.1007/978-3-319-18416-6_64

Cited by 1 publication

(2 citation statements)

References 20 publications

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“…Datasets. Two datasets were used in this work: Gender From Iris (GFI) [30], and ND-CrossSensor-Iris-2013 (CSI) [47]. Both datasets were normalized using OSIRIS [44], which generated images of size 480 × 80 pixels and their corresponding occlusion masks.…”

Section: Datasets and Methodologymentioning

confidence: 99%

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Impact of Occlusion Masks on Gender Classification from Iris Texture

Yáñez,

Tapia,

Perez

et al. 2024

IET Biometrics

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Gender classification on normalized iris images has been previously attempted with varying degrees of success. In these previous studies, it has been shown that occlusion masks may introduce gender information; occlusion masks are used in iris recognition to remove non-iris elements. When, the goal is to classify the gender using exclusively the iris texture, the presence of gender information in the masks may result in apparently higher accuracy, thereby not reflecting the actual gender information present in the iris. However, no measures have been taken to eliminate this information while preserving as much iris information as possible. We propose a novel method to assess the gender information present in the iris more accurately by eliminating gender information in the masks. This consists of pairing iris with similar masks and different gender, generating a paired mask using the OR operator, and applying this mask to the iris. Additionally, we manually fix iris segmentation errors to study their impact on the gender classification. Our results show that occlusion masks can account for 6.92% of the gender classification accuracy on average. Therefore, works aiming to perform gender classification using the iris texture from normalized iris images should eliminate this correlation.

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Section: Datasets and Methodologymentioning

confidence: 99%

“…The second dataset we used is the CSI dataset [47]. This dataset is originally comprised of 29,986 images taken using the LG-4000 NIR sensor, and 116,564 images taken using the LG-2200 NIR sensor.…”

Section: Datasets and Methodologymentioning

confidence: 99%