# Relevant features for Gender Classification in NIR Periocular Images

## TL;DR

This paper identifies the periocular region in NIR images as more informative for gender classification than the iris itself, demonstrating that focusing on this area improves accuracy and speed.

## Contribution

It analyzes and demonstrates the most relevant features in the periocular region for gender classification, introducing a new database and emphasizing the importance of the periocular area over the iris.

## Key findings

- Periocular region contains more gender information than iris.
- Best classification accuracy (89.22%) achieved with 4,000 features.
- Focusing on periocular area enables faster gender classification.

## Abstract

Most gender classifications methods from NIR images have used iris information. Recent work has explored the use of the whole periocular iris region which has surprisingly achieve better results. This suggests the most relevant information for gender classification is not located in the iris as expected. In this work, we analyze and demonstrate the location of the most relevant features that describe gender in periocular NIR images and evaluate its influence its classification. Experiments show that the periocular region contains more gender information than the iris region. We extracted several features (intensity, texture, and shape) and classified them according to its relevance using the XgBoost algorithm. Support Vector Machine and nine ensemble classifiers were used for testing gender accuracy when using the most relevant features. The best classification results were obtained when 4,000 features located on the periocular region were used (89.22\%). Additional experiments with the full periocular iris images versus the iris-Occluded images were performed. The gender classification rates obtained were 84.35\% and 85.75\% respectively. We also contribute to the state of the art with a new database (UNAB-Gender). From results, we suggest focussing only on the surrounding area of the iris. This allows us to realize a faster classification of gender from NIR periocular images.