# Predicting Gender from Iris Texture May Be Harder Than It Seems

arXiv: https://arxiv.org/abs/1811.10066 · HTML: https://mlsanity.com/paper/1811.10066/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Nov 2018.

## Built on by (most cited first, 5 of 5)

- [Investigating Fairness of Ocular Biometrics Among Young, Middle-Aged, and Older Adults](https://mlsanity.com/paper/2110.01641/) (2021)
- [Predicting Gender by First Name Using Character-level Machine Learning](https://mlsanity.com/paper/2106.10156/) (2021)
- [Verification system based on long-range iris and Graph Siamese Neural Networks](https://mlsanity.com/paper/2208.00785/) (2022)
- [Exploring the Feasibility of Deep Learning Techniques for Accurate Gender Classification from Eye Images](https://mlsanity.com/paper/2508.00135/) (2025)
- [Periocular Soft Biometrics: A Survey and Applications to Multimedia Forensics and Disinformation Detection](https://mlsanity.com/paper/2608.14701/) (2026)

## Abstract (arXiv)

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.