# Sex-Prediction from Periocular Images across Multiple Sensors and Spectra

## TL;DR

This study evaluates the accuracy of sex-prediction from periocular images across different sensors and spectra using deep learning, demonstrating promising results in realistic, cross-sensor scenarios.

## Contribution

It introduces a comprehensive analysis of periocular sex-prediction across multiple sensors and spectra, highlighting the feasibility of accurate gender classification in challenging conditions.

## Key findings

- Cross-sensor accuracy ~85% within each spectrum
- Cross-spectral accuracy ~82%
- Multi-spectral accuracy ~83%

## Abstract

In this paper, we provide a comprehensive analysis of periocular-based sex-prediction (commonly referred to as gender classification) using state-of-the-art machine learning techniques. In order to reflect a more challenging scenario where periocular images are likely to be obtained from an unknown source, i.e. sensor, convolutional neural networks are trained on fused sets composed of several near-infrared (NIR) and visible wavelength (VW) image databases. In a cross-sensor scenario within each spectrum an average classification accuracy of approximately 85\% is achieved. When sex-prediction is performed across spectra an average classification accuracy of about 82\% is obtained. Finally, a multi-spectral sex-prediction yields a classification accuracy of 83\% on average. Compared to proposed works, obtained results provide a more realistic estimation of the feasibility to predict a subject's sex from the periocular region.