# Gender-From-Iris or Gender-From-Mascara?

arXiv: https://arxiv.org/abs/1702.01304 · HTML: https://mlsanity.com/paper/1702.01304/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Feb 2017.

## Built on by (most cited first, 9 of 9)

- [Domino: Discovering Systematic Errors with Cross-Modal Embeddings](https://mlsanity.com/paper/2203.14960/) (2022)
- [Discovering Bugs in Vision Models using Off-the-shelf Image Generation and Captioning](https://mlsanity.com/paper/2208.08831/) (2022)
- [Defending Against Image Corruptions Through Adversarial Augmentations](https://mlsanity.com/paper/2104.01086/) (2021)
- [Facial Masks and Soft-Biometrics: Leveraging Face Recognition CNNs for Age and Gender Prediction on Mobile Ocular Images](https://mlsanity.com/paper/2103.16760/) (2021)
- [Benchmarking Robustness to Adversarial Image Obfuscations](https://mlsanity.com/paper/2301.12993/) (2023)
- [Multi-group Learning for Hierarchical Groups](https://mlsanity.com/paper/2402.00258/) (2024)
- [Deep Learning for Iris Recognition: A Review](https://mlsanity.com/paper/2303.08514/) (2023)
- [The Lean Data Scientist: Recent Advances towards Overcoming the Data Bottleneck](https://mlsanity.com/paper/2211.07959/) (2022)
- [Beyond Mortality: Advancements in Post-Mortem Iris Recognition through Data Collection and Computer-Aided Forensic Examination](https://mlsanity.com/paper/2603.26976/) (2026)

## Abstract (arXiv)

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 gender-from-iris texture than has been suggested in previous work.