Predicting Eye Color from Near Infrared Iris Images

Bobeldyk, Denton; Ross, Arun

doi:10.1109/icb2018.2018.00026

Cited by 14 publications

(13 citation statements)

References 18 publications

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“…At the same time, performing classification exclusively on the eyebrow images yielded accuracies from 80-95%. Although this work seems to have been conducted independently and not focused on iris, its results seem to confirm findings of [2,3] and [8], suggesting a majority of gender cues could be located around the eye.…”

Section: Related Worksupporting

confidence: 59%

“…This could cause problems for cases where the iris is not centered in the image. Nevertheless, we believe this approach to be a more adequate implementation of "irisonly" classification than those presented on [2] and [3] Iris, Non-Occlusion Prob. > 70% Non-iris, area equivalent to Non-Occlusion Prob.…”

Section: Localization Of Gender Cuesmentioning

confidence: 98%

“…A person's gender is a soft biometric attribute that, in association with an individual, can be useful for recognition of that particular individual. Gender prediction based on characteristics of the iris is an idea that has been tried during the last decade with results varying from random accuracy up to 97% [17,9,1,15,4,16,2,8,13,14,3,10]. Most of the works used Support Vector Machines (SVM) as classifiers, with different approaches for feature extraction.…”

Section: Related Workmentioning

confidence: 99%

“…More recently, in [3], the same authors used a similar approach, performing cross-dataset and cross-ethnicity evaluations on gender and ethnicity prediction. Reported accuracy for the "Iris-Only" region is about 89%.…”

Section: Related Workmentioning

confidence: 99%

“…There, it is shown that the presence of eye cosmetics and the use of less rigorous cross-validation protocols may have resulted in optimistic estimates of classification accuracy. Furthermore, more recent works [2,14,3,10] suggest that the majority of gender cues is not found in the iris, but in the periocular region that surrounds it. In this sense, our work deepens the understanding of the problem because: a) we use a stricter definition for "iris-only" images; and b) unlike the aforementioned works, we account for the presence of cosmetics, which is known to cause interference in gender classification.…”

Section: Introductionmentioning

confidence: 99%

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Predicting Gender From Iris Texture May Be Harder Than It Seems

Kuehlkamp

Bowyer

2019

2019 IEEE Winter Conference on Applications of Computer Vision (WACV)

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Predicting gender from iris images has been reported by several researchers as an application of machine learning in biometrics. Recent works on this topic have suggested that the preponderance of the gender cues is located in the periocular region rather than in the iris texture itself. This paper focuses on teasing out whether the information for gender prediction is in the texture of the iris stroma, the periocular region, or both. We present a larger dataset for gender from iris, and evaluate gender prediction accuracy using linear SVM and CNN, comparing hand-crafted and deep features. We use probabilistic occlusion masking to gain insight on the problem. Results suggest the discriminative power of the iris texture for gender is weaker than previously thought, and that the gender-related information is primarily in the periocular region.

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Section: Related Worksupporting

confidence: 59%

Section: Localization Of Gender Cuesmentioning

confidence: 98%