# Sex-Classification from Cell-Phones Periocular Iris Images

## TL;DR

This paper demonstrates that super-resolution techniques applied to low-quality selfie periocular iris images significantly improve sex classification accuracy, achieving over 90% accuracy with upscaled images, and introduces a new selfie iris database.

## Contribution

It introduces a super-resolution approach using SRCNNs to enhance periocular iris images from selfies for sex classification and provides a new selfie iris database.

## Key findings

- Resolution increase improves classification accuracy
- Achieved over 90% accuracy with 3x upscaled images
- New selfie iris database with 150 subjects created

## Abstract

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).