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

arXiv: https://arxiv.org/abs/1905.00396 · HTML: https://mlsanity.com/paper/1905.00396/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 May 2019.

## Built on by (most cited first, 1 of 1)

- [Periocular Soft Biometrics: A Survey and Applications to Multimedia Forensics and Disinformation Detection](https://mlsanity.com/paper/2608.14701/) (2026)

## Abstract (arXiv)

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.