Class Representative Autoencoder for Low Resolution Multi-Spectral Gender Classification

TL;DR

This paper introduces AutoGen, a class representative autoencoder model designed to improve gender classification accuracy in low-resolution, multi-spectral face images under unconstrained conditions, outperforming existing methods.

Contribution

The paper presents a novel autoencoder architecture that enhances gender classification by reducing intra-class variability and increasing inter-class differences in challenging low-resolution, multi-spectral face data.

Findings

AutoGen outperforms existing methods on multiple datasets.

Effective in visible and near-infrared spectra.

Improves classification accuracy in unconstrained scenarios.

Abstract

Gender is one of the most common attributes used to describe an individual. It is used in multiple domains such as human computer interaction, marketing, security, and demographic reports. Research has been performed to automate the task of gender recognition in constrained environment using face images, however, limited attention has been given to gender classification in unconstrained scenarios. This work attempts to address the challenging problem of gender classification in multi-spectral low resolution face images. We propose a robust Class Representative Autoencoder model, termed as AutoGen for the same. The proposed model aims to minimize the intra-class variations while maximizing the inter-class variations for the learned feature representations. Results on visible as well as near infrared spectrum data for different resolutions and multiple databases depict the efficacy of the proposed model. Comparative results with existing approaches and two commercial off-the-shelf systems further motivate the use of class representative features for classification.