# Sex-Classification from Cell-Phones Periocular Iris Images

arXiv: https://arxiv.org/abs/1812.11702 · HTML: https://mlsanity.com/paper/1812.11702/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Dec 2018.

## Built on by (most cited first, 6 of 6)

- [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)
- [Selfie Periocular Verification using an Efficient Super-Resolution Approach](https://mlsanity.com/paper/2102.08449/) (2021)
- [Harnessing Unlabeled Data to Improve Generalization of Biometric Gender and Age Classifiers](https://mlsanity.com/paper/2110.04427/) (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)
- [A Study of Gender Classification Techniques Based on Iris Images: A Deep Survey and Analysis](https://mlsanity.com/paper/2508.05246/) (2025)

## Abstract (arXiv)

Selfie soft biometrics has great potential for various applications ranging from marketing, security and online banking. However, it faces many challenges since there is limited control in data acquisition conditions. This chapter presents a Super-Resolution-Convolutional Neural Networks (SRCNNs) approach that increases the resolution of low quality periocular iris images cropped from selfie images of subject's faces. This work shows that increasing image resolution (2x and 3x) can improve the sex-classification rate when using a Random Forest classifier. The best sex-classification rate was 90.15% for the right and 87.15% for the left eye. This was achieved when images were upscaled from 150x150 to 450x450 pixels. These results compare well with the state of the art and show that when improving image resolution with the SRCNN the sex-classification rate increases. Additionally, a novel selfie database captured from 150 subjects with an iPhone X was created (available upon request).