  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.
