  Recent research has explored the possibility of automatically deducing
information such as gender, age and race of an individual from their biometric
data. While the face modality has been extensively studied in this regard, the
iris modality less so. In this paper, we first review the medical literature to
establish a biological basis for extracting gender and race cues from the iris.
Then, we demonstrate that it is possible to use simple texture descriptors,
like BSIF (Binarized Statistical Image Feature) and LBP (Local Binary
Patterns), to extract gender and race attributes from an NIR ocular image used
in a typical iris recognition system. The proposed method predicts gender and
race from a single eye image with an accuracy of 86% and 90%, respectively. In
addition, the following analysis are conducted: (a) the role of different parts
of the ocular region on attribute prediction; (b) the influence of gender on
race prediction, and vice-versa; (c) the impact of eye color on gender and race
prediction; (d) the impact of image blur on gender and race prediction; (e) the
generalizability of the method across different datasets; and (f) the
consistency of prediction performance across the left and right eyes.
