Predicting Gender from Iris Texture May Be Harder than It Seems
Abstract
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.
Cite
Text
Kuehlkamp and Bowyer. "Predicting Gender from Iris Texture May Be Harder than It Seems." IEEE/CVF Winter Conference on Applications of Computer Vision, 2019. doi:10.1109/WACV.2019.00101Markdown
[Kuehlkamp and Bowyer. "Predicting Gender from Iris Texture May Be Harder than It Seems." IEEE/CVF Winter Conference on Applications of Computer Vision, 2019.](https://mlanthology.org/wacv/2019/kuehlkamp2019wacv-predicting/) doi:10.1109/WACV.2019.00101BibTeX
@inproceedings{kuehlkamp2019wacv-predicting,
title = {{Predicting Gender from Iris Texture May Be Harder than It Seems}},
author = {Kuehlkamp, Andrey and Bowyer, Kevin W.},
booktitle = {IEEE/CVF Winter Conference on Applications of Computer Vision},
year = {2019},
pages = {904-912},
doi = {10.1109/WACV.2019.00101},
url = {https://mlanthology.org/wacv/2019/kuehlkamp2019wacv-predicting/}
}