Gender-from-Iris or Gender-from-Mascara?
Abstract
Predicting a person's gender based on the iris texture has been explored by several researchers. This paper considers several dimensions of experimental work on this problem, including person-disjoint train and test, and the effect of cosmetics on eyelash occlusion and imperfect segmentation. We also consider the use of multi-layer perceptron and convolutional neural networks as classifiers, comparing the use of data-driven and hand-crafted features. Our results suggest that the gender-from-iris problem is more difficult than has so far been appreciated. Estimating accuracy using a mean of N person-disjoint train and test partitions, and considering the effect of makeup - a combination of experimental conditions not present in any previous work - we find a much weaker ability to predict genderfrom-iris texture than has been suggested in previous work.
Cite
Text
Kuehlkamp et al. "Gender-from-Iris or Gender-from-Mascara?." IEEE/CVF Winter Conference on Applications of Computer Vision, 2017. doi:10.1109/WACV.2017.133Markdown
[Kuehlkamp et al. "Gender-from-Iris or Gender-from-Mascara?." IEEE/CVF Winter Conference on Applications of Computer Vision, 2017.](https://mlanthology.org/wacv/2017/kuehlkamp2017wacv-gender/) doi:10.1109/WACV.2017.133BibTeX
@inproceedings{kuehlkamp2017wacv-gender,
title = {{Gender-from-Iris or Gender-from-Mascara?}},
author = {Kuehlkamp, Andrey and Becker, Benedict and Bowyer, Kevin W.},
booktitle = {IEEE/CVF Winter Conference on Applications of Computer Vision},
year = {2017},
pages = {1151-1159},
doi = {10.1109/WACV.2017.133},
url = {https://mlanthology.org/wacv/2017/kuehlkamp2017wacv-gender/}
}