  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.
